Vehicle self-localization using particle filters and visual odometry

ABSTRACT

Methods and systems herein can let an autonomous vehicle localize itself precisely and in near real-time in a digital map using visual place recognition. Commercial GPS solutions used in the production of autonomous vehicles generally have very low accuracy. For autonomous driving, the vehicle may need to be able to localize in the map very precisely, for example, within a few centimeters. The method and systems herein incorporate visual place recognition into the digital map and localization process. The roadways or routes within the map can be characterized as a set of nodes, which can be augmented with feature vectors that represent the visual scenes captured using camera sensors. These feature vectors can be constantly updated on the map server and then provided to the vehicles driving the roadways. This process can help create and maintain a diverse set of features for visual place recognition.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefits of and priority, under 35U.S.C. § 119(e), to U.S. Provisional Patent Application Ser. No.62/616,581 filed on Jan. 12, 2018, by Yalla et al., and entitled“Vehicle Self-Localization Using Particle Filters and Visual Odometry,”the entire disclosure of which is incorporated herein by reference, inits entirety, for all that it teaches and for all purposes.

The present application is also a continuation-in-part of U.S. patentapplication Ser. No. 15/798,016 filed on Oct. 30, 2017, by Yalla et al.,and entitled “Visual Place Recognition Based Self-Localization forAutonomous Vehicles,” the entire disclosure of which is incorporatedherein by reference, in its entirety, for all that it teaches and forall purposes.

FIELD

The present disclosure is generally directed to vehicle systems, inparticular, toward autonomous vehicle systems.

BACKGROUND

In recent years, transportation methods have changed substantially. Thischange is due in part to a concern over the limited availability ofnatural resources, a proliferation in personal technology, and asocietal shift to adopt more environmentally friendly transportationsolutions. These considerations have encouraged the development of anumber of new flexible-fuel vehicles, hybrid-electric vehicles, andelectric vehicles.

Generally, vehicles rely on the Global Positioning System (GPS) toprovide location data. Transmissions from the orbiting GPS satellitesallow a vehicle to triangulate the vehicle's position and associate thatposition with a digital map or Geographic Information System (GIS)information to determine the location of the vehicle. Unfortunately, GPSsignals can be interfered with by large buildings, canyons, trees, powerlines, etc. Thus, it is not always possible to receive a GPS signal.Further, the GPS signal is only accurate to a few meters. A self-drivingvehicle can therefore not rely on GPS to determine the exact position ofthe vehicle as a self-driving vehicle may need locate itself within alane or roadway that is only a few meters wide and may travel throughareas that do not receive a GPS signal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a vehicle in accordance with embodiments of the presentdisclosure;

FIG. 2 shows a plan view of the vehicle in accordance with at least someembodiments of the present disclosure;

FIG. 3A is a block diagram of an embodiment of a communicationenvironment of the vehicle in accordance with embodiments of the presentdisclosure;

FIG. 3B is a block diagram of an embodiment of interior sensors withinthe vehicle in accordance with embodiments of the present disclosure;

FIG. 3C is a block diagram of an embodiment of a navigation system ofthe vehicle in accordance with embodiments of the present disclosure;

FIG. 4 shows an embodiment of the instrument panel of the vehicleaccording to one embodiment of the present disclosure;

FIG. 5 is a block diagram of an embodiment of a communications subsystemof the vehicle;

FIG. 6 is a block diagram of a computing environment associated with theembodiments presented herein;

FIG. 7 is a block diagram of a computing device associated with one ormore components described herein;

FIG. 8A shows a visual representation of an embodiment of a vehiclelocalization system in accordance with embodiments of the presentdisclosure;

FIG. 8B shows another visual representation of an embodiment of avehicle localization system in accordance with embodiments of thepresent disclosure;

FIG. 8C shows another visual representation of an embodiment of avehicle localization system in accordance with embodiments of thepresent disclosure;

FIG. 8D shows another visual representation of an embodiment of avehicle localization system in accordance with embodiments of thepresent disclosure;

FIG. 8E shows another visual representation of an embodiment of avehicle localization system in accordance with embodiments of thepresent disclosure;

FIG. 8F shows another visual representation of an embodiment of avehicle localization system in accordance with embodiments of thepresent disclosure;

FIG. 8G shows another visual representation of an embodiment of avehicle localization system in accordance with embodiments of thepresent disclosure;

FIG. 8H shows another visual representation of an embodiment of avehicle localization system in accordance with embodiments of thepresent disclosure;

FIG. 9A is a diagram of an embodiment of a data store that storeslocalization data in accordance with embodiments of the presentdisclosure;

FIG. 9B is a diagram of an embodiment of a data structure that storeslocalization data in accordance with embodiments of the presentdisclosure;

FIG. 9C is another diagram of an embodiment of another data structurethat stores localization data in accordance with embodiments of thepresent disclosure;

FIG. 10 is a process diagram of an embodiment of a method for creatingsegment information for determining the local position of a vehicle inaccordance with embodiments of the present disclosure;

FIG. 11 is a process diagram of an embodiment of a method fordetermining the local position of a vehicle in accordance withembodiments of the present disclosure;

FIG. 12 is a process diagram of an embodiment of a method for creatingsegment information for determining the local position of a vehicle inaccordance with alternative embodiments of the present disclosure; and

FIG. 13 is a process diagram of an embodiment of a method fordetermining the local position of a vehicle in accordance withalternative embodiments of the present disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure will be described in connectionwith a vehicle, and in some embodiments, a self-driving vehicle. Methodsand systems herein can let an autonomous vehicle localize itselfprecisely and in near real-time in a digital map using visual placerecognition. Commercial GPS solutions used in the production ofautonomous vehicles generally have very low accuracy. For autonomousdriving, the vehicle may need to be able to localize in the map veryprecisely, for example, within a few centimeters. Additionally, in urbanareas with high-rise buildings, the vehicles will face problems withpoor GPS Signal reception.

The embodiments described herein solve these and other problems byincorporating visual place recognition into the digital map andlocalization process. The roadways or routes within the map can becharacterized as a set of nodes. The nodes in the map can be augmentedwith feature vectors that represent the visual scenes captured usingcamera sensors or other sensors. These feature vectors can be constantlyupdated on the map server, based on the real-time information that isbeing generated by the vehicles (both autonomous and non-autonomous) onthe different road segments, and then provided to the vehicles drivingthe roadways. This process can help create and maintain a diverse set offeatures for visual place recognition to counter the situations where ascene environment can be changing due to weather, construction, or otherphenomenon.

For the self-localization of the autonomous vehicle, a combination ofapproaches, for example, particle-filtering and/or visual odometry, maybe used to identify potential current positions of the vehicle and usevisual feature matching (visual place recognition) to aid with fasterand more efficient solution. The localization of the vehicle may be aniterative process and may take a short amount of time (e.g., a fewseconds) to converge to a location on the map. The use of visual placerecognition, by embedding the feature vectors into the map and doingreal-time feature comparison, expedites this iterative localizationprocess by huge margin, for example, to milliseconds.

The above methods are an improvement over other existing localizationmethods. For example, some other systems use a Mixture ofGaussians-based approach to localize based purely on visual odometryinformation. This Mixture of Gaussians approach has issues convergingfaster to a precise location on the map as there are number of potentialroad segments that could have similar road topology giving ambiguousvisual odometry segments for matching with the map information. In theembodiments herein, visual place recognition can augment the segmentnodes in the High Definition (HD) maps. At each map node, the navigationsystem or map system can store a feature vector captured using thecamera sensors. As the localization algorithm tries to localize thecurrent position, the navigation system can continuously capture imagesusing the camera or other sensors, may then extract the image featurevectors, and may then match the feature vectors that are captured by thesensors with the feature vectors that are currently stored at each nodein the map. Thereby, the likely number of possible locations where thevehicle may be located is reduced.

Other existing system may use a three-dimensional (3D) Light Detectionand Ranging (LiDAR) point cloud-based matching for localization. Thedigital map itself, in these 3D LiDAR systems, is created using dense 3Dpoint clouds. The dense 3D point cloud-based maps are created bycapturing 3D points using LiDARs and mapping the same road segmentmultiple times. The embodiments herein do depend on these dense 3D pointclouds but can employ a HD map that can be generated from these dense 3Dpoint clouds. Dense 3D point cloud-based maps are not practical todeploy in a production environment due to the huge computational andmemory footprint requirements, which are impractical to place in avehicle.

FIG. 1 shows a perspective view of a vehicle 100 in accordance withembodiments of the present disclosure. The electric vehicle 100comprises a vehicle front 110, vehicle aft or rear 120, vehicle roof130, at least one vehicle side 160, a vehicle undercarriage 140, and avehicle interior 150. In any event, the vehicle 100 may include a frame104 and one or more body panels 108 mounted or affixed thereto. Thevehicle 100 may include one or more interior components (e.g.,components inside an interior space 150, or user space, of a vehicle100, etc.), exterior components (e.g., components outside of theinterior space 150, or user space, of a vehicle 100, etc.), drivesystems, controls systems, structural components, etc.

Although shown in the form of a car, it should be appreciated that thevehicle 100 described herein may include any conveyance or model of aconveyance, where the conveyance was designed for the purpose of movingone or more tangible objects, such as people, animals, cargo, and thelike. The term “vehicle” does not require that a conveyance moves or iscapable of movement. Typical vehicles may include but are in no waylimited to cars, trucks, motorcycles, busses, automobiles, trains,railed conveyances, boats, ships, marine conveyances, submarineconveyances, airplanes, space craft, flying machines, human-poweredconveyances, and the like.

In some embodiments, the vehicle 100 may include a number of sensors,devices, and/or systems that are capable of assisting in drivingoperations, e.g., autonomous or semi-autonomous control. Examples of thevarious sensors and systems may include, but are in no way limited to,one or more of cameras (e.g., independent, stereo, combined image,etc.), InfraRed (IR) sensors, Radio Frequency (RF) sensors, ultrasonicsensors (e.g., transducers, transceivers, etc.), RADAR sensors (e.g.,object-detection sensors and/or systems), Light Imaging, Detection, AndRanging (LIDAR) systems, odometry sensors and/or devices (e.g.,encoders, etc.), orientation sensors (e.g., accelerometers, gyroscopes,magnetometer, etc.), navigation sensors and systems (e.g., GPS, etc.),and other ranging, imaging, and/or object-detecting sensors. The sensorsmay be disposed in an interior space 150 of the vehicle 100 and/or on anoutside of the vehicle 100. In some embodiments, the sensors and systemsmay be disposed in one or more portions of a vehicle 100 (e.g., theframe 104, a body panel, a compartment, etc.).

The vehicle sensors and systems may be selected and/or configured tosuit a level of operation associated with the vehicle 100. Among otherthings, the number of sensors used in a system may be altered toincrease or decrease information available to a vehicle control system(e.g., affecting control capabilities of the vehicle 100). Additionally,or alternatively, the sensors and systems may be part of one or moreAdvanced Driver Assistance Systems (ADAS) associated with a vehicle 100.In any event, the sensors and systems may be used to provide drivingassistance at any level of operation (e.g., from fully-manual tofully-autonomous operations, etc.) as described herein.

The various levels of vehicle control and/or operation can be describedas corresponding to a level of autonomy associated with a vehicle 100for vehicle driving operations. For instance, at Level 0, orfully-manual driving operations, a driver (e.g., a human driver) may beresponsible for all the driving control operations (e.g., steering,accelerating, braking, etc.) associated with the vehicle. Level 0 may bereferred to as a “No Automation” level. At Level 1, the vehicle may beresponsible for a limited number of the driving operations associatedwith the vehicle, while the driver is still responsible for most drivingcontrol operations. An example of a Level 1 vehicle may include avehicle in which the throttle control and/or braking operations may becontrolled by the vehicle (e.g., cruise control operations, etc.). Level1 may be referred to as a “Driver Assistance” level. At Level 2, thevehicle may collect information (e.g., via one or more drivingassistance systems, sensors, etc.) about an environment of the vehicle(e.g., surrounding area, roadway, traffic, ambient conditions, etc.) anduse the collected information to control driving operations (e.g.,steering, accelerating, braking, etc.) associated with the vehicle. In aLevel 2 autonomous vehicle, the driver may be required to perform otheraspects of driving operations not controlled by the vehicle. Level 2 maybe referred to as a “Partial Automation” level. It should be appreciatedthat Levels 0-2 all involve the driver monitoring the driving operationsof the vehicle.

At Level 3, the driver may be separated from controlling all the drivingoperations of the vehicle except when the vehicle makes a request forthe operator to act or intervene in controlling one or more drivingoperations. In other words, the driver may be separated from controllingthe vehicle unless the driver is required to take over for the vehicle.Level 3 may be referred to as a “Conditional Automation” level. At Level4, the driver may be separated from controlling all the drivingoperations of the vehicle and the vehicle may control driving operationseven when a user fails to respond to a request to intervene. Level 4 maybe referred to as a “High Automation” level. At Level 5, the vehicle cancontrol all the driving operations associated with the vehicle in alldriving modes. The vehicle in Level 5 may continually monitor traffic,vehicular, roadway, and/or environmental conditions while driving thevehicle. In Level 5, there is no human driver interaction required inany driving mode. Accordingly, Level 5 may be referred to as a “FullAutomation” level. It should be appreciated that in Levels 3-5 thevehicle, and/or one or more automated driving systems associated withthe vehicle, monitors the driving operations of the vehicle and thedriving environment.

As shown in FIG. 1, the vehicle 100 may, for example, include at leastone of a ranging and imaging system 112 (e.g., LIDAR, etc.), an imagingsensor 116A, 116F (e.g., camera, IR, etc.), a radio object-detection andranging system sensors 116B (e.g., RADAR, RF, etc.), ultrasonic sensors116C, and/or other object-detection sensors 116D, 116E. In someembodiments, the LIDAR system 112 and/or sensors may be mounted on aroof 130 of the vehicle 100. In one embodiment, the RADAR sensors 116Bmay be disposed at least at a front 110, aft 120, or side 160 of thevehicle 100. Among other things, the RADAR sensors may be used tomonitor and/or detect a position of other vehicles, pedestrians, and/orother objects near, or proximal to, the vehicle 100. While shownassociated with one or more areas of a vehicle 100, it should beappreciated that any of the sensors and systems 116A-K, 112 illustratedin FIGS. 1 and 2 may be disposed in, on, and/or about the vehicle 100 inany position, area, and/or zone of the vehicle 100.

Referring now to FIG. 2, a plan view of a vehicle 100 will be describedin accordance with embodiments of the present disclosure. In particular,FIG. 2 shows a vehicle sensing environment 200 at least partiallydefined by the sensors and systems 116A-K, 112 disposed in, on, and/orabout the vehicle 100. Each sensor 116A-K may include an operationaldetection range R and operational detection angle. The operationaldetection range R may define the effective detection limit, or distance,of the sensor 116A-K. In some cases, this effective detection limit maybe defined as a distance from a portion of the sensor 116A-K (e.g., alens, sensing surface, etc.) to a point in space offset from the sensor116A-K. The effective detection limit may define a distance, beyondwhich, the sensing capabilities of the sensor 116A-K deteriorate, failto work, or are unreliable. In some embodiments, the effective detectionlimit may define a distance, within which, the sensing capabilities ofthe sensor 116A-K are able to provide accurate and/or reliable detectioninformation. The operational detection angle may define at least oneangle of a span, or between horizontal and/or vertical limits, of asensor 116A-K. As can be appreciated, the operational detection limitand the operational detection angle of a sensor 116A-K together maydefine the effective detection zone 216A-D (e.g., the effectivedetection area, and/or volume, etc.) of a sensor 116A-K.

In some embodiments, the vehicle 100 may include a ranging and imagingsystem 112 such as LIDAR, or the like. The ranging and imaging system112 may be configured to detect visual information in an environmentsurrounding the vehicle 100. The visual information detected in theenvironment surrounding the ranging and imaging system 112 may beprocessed (e.g., via one or more sensor and/or system processors, etc.)to generate a complete 360-degree view of an environment 200 around thevehicle. The ranging and imaging system 112 may be configured togenerate changing 360-degree views of the environment 200 in real-time,for instance, as the vehicle 100 drives. In some cases, the ranging andimaging system 112 may have an effective detection limit 204 that issome distance from the center of the vehicle 100 outward over 360degrees. The effective detection limit 204 of the ranging and imagingsystem 112 defines a view zone 208 (e.g., an area and/or volume, etc.)surrounding the vehicle 100. Any object falling outside of the view zone208 is in the undetected zone 212 and would not be detected by theranging and imaging system 112 of the vehicle 100.

Sensor data and information may be collected by one or more sensors orsystems 116A-K, 112 of the vehicle 100 monitoring the vehicle sensingenvironment 200. This information may be processed (e.g., via aprocessor, computer-vision system, etc.) to determine targets (e.g.,objects, signs, people, markings, roadways, conditions, etc.) inside oneor more detection zones 208, 216A-D associated with the vehicle sensingenvironment 200. In some cases, information from multiple sensors 116A-Kmay be processed to form composite sensor detection information. Forexample, a first sensor 116A and a second sensor 116F may correspond toa first camera 116A and a second camera 116F aimed in a forwardtraveling direction of the vehicle 100. In this example, imagescollected by the cameras 116A, 116F may be combined to form stereo imageinformation. This composite information may increase the capabilities ofa single sensor in the one or more sensors 116A-K by, for example,adding the ability to determine depth associated with targets in the oneor more detection zones 208, 216A-D. Similar image data may be collectedby rear view cameras (e.g., sensors 116G, 116H) aimed in a rearwardtraveling direction vehicle 100.

In some embodiments, multiple sensors 116A-K may be effectively joinedto increase a sensing zone and provide increased sensing coverage. Forinstance, multiple RADAR sensors 116B disposed on the front 110 of thevehicle may be joined to provide a zone 216B of coverage that spansacross an entirety of the front 110 of the vehicle. In some cases, themultiple RADAR sensors 116B may cover a detection zone 216B thatincludes one or more other sensor detection zones 216A. Theseoverlapping detection zones may provide redundant sensing, enhancedsensing, and/or provide greater detail in sensing within a particularportion (e.g., zone 216A) of a larger zone (e.g., zone 216B).Additionally, or alternatively, the sensors 116A-K of the vehicle 100may be arranged to create a complete coverage, via one or more sensingzones 208, 216A-D around the vehicle 100. In some areas, the sensingzones 216C of two or more sensors 116D, 116E may intersect at an overlapzone 220. In some areas, the angle and/or detection limit of two or moresensing zones 216C, 216D (e.g., of two or more sensors 116E, 116J, 116K)may meet at a virtual intersection point 224.

The vehicle 100 may include a number of sensors 116E, 116G, 116H, 116J,116K disposed proximal to the rear 120 of the vehicle 100. These sensorscan include, but are in no way limited to, an imaging sensor, camera,IR, a radio object-detection and ranging sensors, RADAR, RF, ultrasonicsensors, and/or other object-detection sensors. Among other things,these sensors 116E, 116G, 116H, 116J, 116K may detect targets near orapproaching the rear of the vehicle 100. For example, another vehicleapproaching the rear 120 of the vehicle 100 may be detected by one ormore of the ranging and imaging system (e.g., LIDAR) 112, rear-viewcameras 116G, 116H, and/or rear facing RADAR sensors 116J, 116K. Asdescribed above, the images from the rear-view cameras 116G, 116H may beprocessed to generate a stereo view (e.g., providing depth associatedwith an object or environment, etc.) for targets visible to both cameras116G, 116H. As another example, the vehicle 100 may be driving and oneor more of the ranging and imaging system 112, front-facing cameras116A, 116F, front-facing RADAR sensors 116B, and/or ultrasonic sensors116C may detect targets in front of the vehicle 100. This approach mayprovide critical sensor information to a vehicle control system in atleast one of the autonomous driving levels described above. Forinstance, when the vehicle 100 is driving autonomously (e.g., Level 3,Level 4, or Level 5) and detects other vehicles stopped in a travelpath, the sensor detection information may be sent to the vehiclecontrol system of the vehicle 100 to control a driving operation (e.g.,braking, decelerating, etc.) associated with the vehicle 100 (in thisexample, slowing the vehicle 100 as to avoid colliding with the stoppedother vehicles). As yet another example, the vehicle 100 may beoperating and one or more of the ranging and imaging system 112, and/orthe side-facing sensors 116D, 116E (e.g., RADAR, ultrasonic, camera,combinations thereof, and/or other type of sensor), may detect targetsat a side of the vehicle 100. It should be appreciated that the sensors116A-K may detect a target that is both at a side 160 and a front 110 ofthe vehicle 100 (e.g., disposed at a diagonal angle to a centerline ofthe vehicle 100 running from the front 110 of the vehicle 100 to therear 120 of the vehicle). Additionally, or alternatively, the sensors116A-K may detect a target that is both, or simultaneously, at a side160 and a rear 120 of the vehicle 100 (e.g., disposed at a diagonalangle to the centerline of the vehicle 100).

FIGS. 3A-3C are block diagrams of an embodiment of a communicationenvironment 300 of the vehicle 100 in accordance with embodiments of thepresent disclosure. The communication system 300 may include one or morevehicle driving vehicle sensors and systems 304, sensor processors 340,sensor data memory 344, vehicle control system 348, communicationssubsystem 350, control data 364, computing devices 368, display devices372, and other components 374 that may be associated with a vehicle 100.These associated components may be electrically and/or communicativelycoupled to one another via at least one bus 360. In some embodiments,the one or more associated components may send and/or receive signalsacross a communication network 352 to at least one of a navigationsource 356A, a control source 356B, or some other entity 356N.

In accordance with at least some embodiments of the present disclosure,the communication network 352 may comprise any type of knowncommunication medium or collection of communication media and may useany type of protocols, such as SIP, TCP/IP, SNA, IPX, AppleTalk, and thelike, to transport messages between endpoints. The communication network352 may include wired and/or wireless communication technologies. TheInternet is an example of the communication network 352 that constitutesan Internet Protocol (IP) network consisting of many computers,computing networks, and other communication devices located all over theworld, which are connected through many telephone systems and othermeans. Other examples of the communication network 352 include, withoutlimitation, a standard Plain Old Telephone System (POTS), an IntegratedServices Digital Network (ISDN), the Public Switched Telephone Network(PSTN), a Local Area Network (LAN), such as an Ethernet network, aToken-Ring network and/or the like, a Wide Area Network (WAN), a virtualnetwork, including without limitation a Virtual Private Network (VPN);the Internet, an intranet, an extranet, a cellular network, an infra-rednetwork; a wireless network (e.g., a network operating under any of theIEEE 802.9 suite of protocols, the Bluetooth® protocol known in the art,and/or any other wireless protocol), and any other type ofpacket-switched or circuit-switched network known in the art and/or anycombination of these and/or other networks. In addition, it can beappreciated that the communication network 352 need not be limited toany one network type, and instead may be comprised of a number ofdifferent networks and/or network types. The communication network 352may comprise a number of different communication media such as coaxialcable, copper cable/wire, fiber-optic cable, antennas fortransmitting/receiving wireless messages, and combinations thereof.

The driving vehicle sensors and systems 304 may include at least onenavigation 308 (e.g., Global Positioning System (GPS), etc.),orientation 312, odometry 316, LIDAR 320, RADAR 324, ultrasonic 328,camera 332, InfraRed (IR) 336, and/or other sensor or system 338. Thesedriving vehicle sensors and systems 304 may be similar, if notidentical, to the sensors and systems 116A-K, 112 described inconjunction with FIGS. 1 and 2.

The navigation sensor 308 may include one or more sensors havingreceivers and antennas that are configured to utilize a satellite-basednavigation system including a network of navigation satellites capableof providing geolocation and time information to at least one componentof the vehicle 100. Examples of the navigation sensor 308 as describedherein may include, but are not limited to, at least one of Garmin® GLO™family of GPS and GLONASS combination sensors, Garmin® GPS 15x™ familyof sensors, Garmin® GPS 16x™ family of sensors with high-sensitivityreceiver and antenna, Garmin® GPS 18x OEM family of high-sensitivity GPSsensors, Dewetron DEWE-VGPS series of GPS sensors, GlobalSat 1-Hz seriesof GPS sensors, other industry-equivalent navigation sensors and/orsystems, and may perform navigational and/or geolocation functions usingany known or future-developed standard and/or architecture.

The orientation sensor 312 may include one or more sensors configured todetermine an orientation of the vehicle 100 relative to at least onereference point. In some embodiments, the orientation sensor 312 mayinclude at least one pressure transducer, stress/strain gauge,accelerometer, gyroscope, and/or geomagnetic sensor. Examples of thenavigation sensor 308 as described herein may include, but are notlimited to, at least one of Bosch Sensortec BMX 160 series low-powerabsolute orientation sensors, Bosch Sensortec BMX055 9-axis sensors,Bosch Sensortec BMI055 6-axis inertial sensors, Bosch Sensortec BMI1606-axis inertial sensors, Bosch Sensortec BMF055 9-axis inertial sensors(accelerometer, gyroscope, and magnetometer) with integrated Cortex M0+microcontroller, Bosch Sensortec BMP280 absolute barometric pressuresensors, Infineon TLV493D-A1B6 3D magnetic sensors, InfineonTLI493D-W1B6 3D magnetic sensors, Infineon TL family of 3D magneticsensors, Murata Electronics SCC2000 series combined gyro sensor andaccelerometer, Murata Electronics SCC1300 series combined gyro sensorand accelerometer, other industry-equivalent orientation sensors and/orsystems, which may perform orientation detection and/or determinationfunctions using any known or future-developed standard and/orarchitecture.

The odometry sensor and/or system 316 may include one or more componentsthat is configured to determine a change in position of the vehicle 100over time. In some embodiments, the odometry system 316 may utilize datafrom one or more other sensors and/or systems 304 in determining aposition (e.g., distance, location, etc.) of the vehicle 100 relative toa previously measured position for the vehicle 100. Additionally, oralternatively, the odometry sensors 316 may include one or moreencoders, Hall speed sensors, and/or other measurement sensors/devicesconfigured to measure a wheel speed, rotation, and/or number ofrevolutions made over time. Examples of the odometry sensor/system 316as described herein may include, but are not limited to, at least one ofInfineon TLE4924/26/27/28C high-performance speed sensors, InfineonTL4941plusC(B) single chip differential Hall wheel-speed sensors,Infineon TL5041plusC Giant Magnetoresistance (GMR) effect sensors,Infineon TL family of magnetic sensors, EPC Model 25SP Accu-CoderPro™incremental shaft encoders, EPC Model 30M compact incremental encoderswith advanced magnetic sensing and signal processing technology, EPCModel 925 absolute shaft encoders, EPC Model 958 absolute shaftencoders, EPC Model MA36S/MA63S/SA36S absolute shaft encoders, Dynapar™F18 commutating optical encoder, Dynapar™ HS35R family of phased arrayencoder sensors, other industry-equivalent odometry sensors and/orsystems, and may perform change in position detection and/ordetermination functions using any known or future-developed standardand/or architecture.

The LIDAR sensor/system 320 may include one or more componentsconfigured to measure distances to targets using laser illumination. Insome embodiments, the LIDAR sensor/system 320 may provide 3D imagingdata of an environment around the vehicle 100. The imaging data may beprocessed to generate a full 360-degree view of the environment aroundthe vehicle 100. The LIDAR sensor/system 320 may include a laser lightgenerator configured to generate a plurality of target illuminationlaser beams (e.g., laser light channels). In some embodiments, thisplurality of laser beams may be aimed at, or directed to, a rotatingreflective surface (e.g., a mirror) and guided outwardly from the LIDARsensor/system 320 into a measurement environment. The rotatingreflective surface may be configured to continually rotate 360 degreesabout an axis, such that the plurality of laser beams is directed in afull 360-degree range around the vehicle 100. A photodiode receiver ofthe LIDAR sensor/system 320 may detect when light from the plurality oflaser beams emitted into the measurement environment returns (e.g.,reflected echo) to the LIDAR sensor/system 320. The LIDAR sensor/system320 may calculate, based on a time associated with the emission of lightto the detected return of light, a distance from the vehicle 100 to theilluminated target. In some embodiments, the LIDAR sensor/system 320 maygenerate over 2.0 million points per second and have an effectiveoperational range of at least 100 meters. Examples of the LIDARsensor/system 320 as described herein may include, but are not limitedto, at least one of Velodyne® LiDAR™ HDL-64E 64-channel LIDAR sensors,Velodyne® LiDAR™ HDL-32E 32-channel LIDAR sensors, Velodyne® LiDAR™PUCK™ VLP-16 16-channel LIDAR sensors, Leica Geosystems Pegasus:Twomobile sensor platform, Garmin® LIDAR-Lite v3 measurement sensor,Quanergy M8 LiDAR sensors, Quanergy S3 solid state LiDAR sensor,LeddarTech® LeddarVU compact solid state fixed-beam LIDAR sensors, otherindustry-equivalent LIDAR sensors and/or systems, and may performilluminated target and/or obstacle detection in an environment aroundthe vehicle 100 using any known or future-developed standard and/orarchitecture.

The RADAR sensors 324 may include one or more radio components that areconfigured to detect objects/targets in an environment of the vehicle100. In some embodiments, the RADAR sensors 324 may determine adistance, position, and/or movement vector (e.g., angle, speed, etc.)associated with a target over time. The RADAR sensors 324 may include atransmitter configured to generate and emit electromagnetic waves (e.g.,radio, microwaves, etc.) and a receiver configured to detect returnedelectromagnetic waves. In some embodiments, the RADAR sensors 324 mayinclude at least one processor configured to interpret the returnedelectromagnetic waves and determine locational properties of targets.Examples of the RADAR sensors 324 as described herein may include, butare not limited to, at least one of Infineon RASIC™ RTN7735PLtransmitter and RRN7745PL/46PL receiver sensors, Autoliv ASP VehicleRADAR sensors, Delphi L2C0051TR 77 GHz ESR Electronically Scanning Radarsensors, Fujitsu Ten Ltd. Automotive Compact 77 GHz 3D Electronic ScanMillimeter Wave Radar sensors, other industry-equivalent RADAR sensorsand/or systems, and may perform radio target and/or obstacle detectionin an environment around the vehicle 100 using any known orfuture-developed standard and/or architecture.

The ultrasonic sensors 328 may include one or more components that areconfigured to detect objects/targets in an environment of the vehicle100. In some embodiments, the ultrasonic sensors 328 may determine adistance, position, and/or movement vector (e.g., angle, speed, etc.)associated with a target over time. The ultrasonic sensors 328 mayinclude an ultrasonic transmitter and receiver, or transceiver,configured to generate and emit ultrasound waves and interpret returnedechoes of those waves. In some embodiments, the ultrasonic sensors 328may include at least one processor configured to interpret the returnedultrasonic waves and determine locational properties of targets.Examples of the ultrasonic sensors 328 as described herein may include,but are not limited to, at least one of Texas Instruments TIDA-00151automotive ultrasonic sensor interface IC sensors, MaxBotix® MB8450ultrasonic proximity sensor, MaxBotix® ParkSonar™-EZ ultrasonicproximity sensors, Murata Electronics MA40H1S-R open-structureultrasonic sensors, Murata Electronics MA40S4R/S open-structureultrasonic sensors, Murata Electronics MA58MF14-7N waterproof ultrasonicsensors, other industry-equivalent ultrasonic sensors and/or systems,and may perform ultrasonic target and/or obstacle detection in anenvironment around the vehicle 100 using any known or future-developedstandard and/or architecture.

The camera sensors 332 may include one or more components configured todetect image information associated with an environment of the vehicle100. In some embodiments, the camera sensors 332 may include a lens,filter, image sensor, and/or a digital image processor. It is an aspectof the present disclosure that multiple camera sensors 332 may be usedtogether to generate stereo images providing depth measurements.Examples of the camera sensors 332 as described herein may include, butare not limited to, at least one of ON Semiconductor® MT9V024 GlobalShutter VGA GS CMOS image sensors, Teledyne DALSA Falcon2 camerasensors, CMOSIS CMV50000 high-speed CMOS image sensors, otherindustry-equivalent camera sensors and/or systems, and may performvisual target and/or obstacle detection in an environment around thevehicle 100 using any known or future-developed standard and/orarchitecture.

The InfraRed (IR) sensors 336 may include one or more componentsconfigured to detect image information associated with an environment ofthe vehicle 100. The IR sensors 336 may be configured to detect targetsin low-light, dark, or poorly-lit environments. The IR sensors 336 mayinclude an IR light emitting element (e.g., IR Light Emitting Diode(LED), etc.) and an IR photodiode. In some embodiments, the IRphotodiode may be configured to detect returned IR light at or about thesame wavelength to that emitted by the IR light emitting element. Insome embodiments, the IR sensors 336 may include at least one processorconfigured to interpret the returned IR light and determine locationalproperties of targets. The IR sensors 336 may be configured to detectand/or measure a temperature associated with a target (e.g., an object,pedestrian, other vehicle, etc.). Examples of IR sensors 336 asdescribed herein may include, but are not limited to, at least one ofOpto Diode lead-salt IR array sensors, Opto Diode OD-850 Near-IR LEDsensors, Opto Diode SA/SHA727 steady state IR emitters and IR detectors,FLIR® LS microbolometer sensors, FLIR® TacFLIR 380-HD InSb MWIR FPA andHD MWIR thermal sensors, FLIR® VOx 640×480 pixel detector sensors,Delphi IR sensors, other industry-equivalent IR sensors and/or systems,and may perform IR visual target and/or obstacle detection in anenvironment around the vehicle 100 using any known or future-developedstandard and/or architecture.

The vehicle 100 can also include one or more interior sensors 337.Interior sensors 337 can measure characteristics of the insideenvironment of the vehicle 100. The interior sensors 337 may be asdescribed in conjunction with FIG. 3B.

A navigation system 302 can include any hardware and/or software used tonavigate the vehicle either manually or autonomously. The navigationsystem 302 may be as described in conjunction with FIG. 3C.

In some embodiments, the driving vehicle sensors and systems 304 mayinclude other sensors 338 and/or combinations of the sensors 306-337described above. Additionally, or alternatively, one or more of thesensors 306-337 described above may include one or more processorsconfigured to process and/or interpret signals detected by the one ormore sensors 306-337. In some embodiments, the processing of at leastsome sensor information provided by the vehicle sensors and systems 304may be processed by at least one sensor processor 340. Raw and/orprocessed sensor data may be stored in a sensor data memory 344 storagemedium. In some embodiments, the sensor data memory 344 may storeinstructions used by the sensor processor 340 for processing sensorinformation provided by the sensors and systems 304. In any event, thesensor data memory 344 may be a disk drive, optical storage device,solid-state storage device such as a Random-Access Memory (RAM) and/or aRead-Only Memory (ROM), which can be programmable, flash-updateable,and/or the like.

The vehicle control system 348 may receive processed sensor informationfrom the sensor processor 340 and determine to control an aspect of thevehicle 100. Controlling an aspect of the vehicle 100 may includepresenting information via one or more display devices 372 associatedwith the vehicle, sending commands to one or more computing devices 368associated with the vehicle, and/or controlling a driving operation ofthe vehicle. In some embodiments, the vehicle control system 348 maycorrespond to one or more computing systems that control drivingoperations of the vehicle 100 in accordance with the Levels of drivingautonomy described above. In one embodiment, the vehicle control system348 may operate a speed of the vehicle 100 by controlling an outputsignal to the accelerator and/or braking system of the vehicle. In thisexample, the vehicle control system 348 may receive sensor datadescribing an environment surrounding the vehicle 100 and, based on thesensor data received, determine to adjust the acceleration, poweroutput, and/or braking of the vehicle 100. The vehicle control system348 may additionally control steering and/or other driving functions ofthe vehicle 100.

The vehicle control system 348 may communicate, in real-time, with thedriving sensors and systems 304 forming a feedback loop. In particular,upon receiving sensor information describing a condition of targets inthe environment surrounding the vehicle 100, the vehicle control system348 may autonomously make changes to a driving operation of the vehicle100. The vehicle control system 348 may then receive subsequent sensorinformation describing any change to the condition of the targetsdetected in the environment as a result of the changes made to thedriving operation. This continual cycle of observation (e.g., via thesensors, etc.) and action (e.g., selected control or non-control ofvehicle operations, etc.) allows the vehicle 100 to operate autonomouslyin the environment.

In some embodiments, the one or more components of the vehicle 100(e.g., the driving vehicle sensors 304, vehicle control system 348,display devices 372, etc.) may communicate across the communicationnetwork 352 to one or more entities 356A-N via a communicationssubsystem 350 of the vehicle 100. Embodiments of the communicationssubsystem 350 are described in greater detail in conjunction with FIG.5. For instance, the navigation sensors 308 may receive globalpositioning, location, and/or navigational information from a navigationsource 356A. In some embodiments, the navigation source 356A may be aglobal navigation satellite system (GNSS) similar, if not identical, toNAVSTAR GPS, GLONASS, EU Galileo, and/or the BeiDou Navigation SatelliteSystem (BDS) to name a few.

In some embodiments, the vehicle control system 348 may receive controlinformation from one or more control sources 356B. The control source356 may provide vehicle control information including autonomous drivingcontrol commands, vehicle operation override control commands, and thelike. The control source 356 may correspond to an autonomous vehiclecontrol system, a traffic control system, an administrative controlentity, and/or some other controlling server. It is an aspect of thepresent disclosure that the vehicle control system 348 and/or othercomponents of the vehicle 100 may exchange communications with thecontrol source 356 across the communication network 352 and via thecommunications subsystem 350.

Information associated with controlling driving operations of thevehicle 100 may be stored in a control data memory 364 storage medium.The control data memory 364 may store instructions used by the vehiclecontrol system 348 for controlling driving operations of the vehicle100, historical control information, autonomous driving control rules,and the like. In some embodiments, the control data memory 364 may be adisk drive, optical storage device, solid-state storage device such as aRandom-Access memory (RAM) and/or a Read-Only Memory (ROM), which can beprogrammable, flash-updateable, and/or the like.

In addition to the mechanical components described herein, the vehicle100 may include a number of user interface devices. The user interfacedevices receive and translate human input into a mechanical movement orelectrical signal or stimulus. The human input may be one or more ofmotion (e.g., body movement, body part movement, in two-dimensional orthree-dimensional space, etc.), voice, touch, and/or physicalinteraction with the components of the vehicle 100. In some embodiments,the human input may be configured to control one or more functions ofthe vehicle 100 and/or systems of the vehicle 100 described herein. Userinterfaces may include, but are in no way limited to, at least onegraphical user interface of a display device, steering wheel ormechanism, transmission lever or button (e.g., including park, neutral,reverse, and/or drive positions, etc.), throttle control pedal ormechanism, brake control pedal or mechanism, power control switch,communications equipment, etc.

FIG. 3B shows a block diagram of an embodiment of interior sensors 337for a vehicle 100. The interior sensors 337 may be arranged into one ormore groups, based at least partially on the function of the interiorsensors 337. For example, the interior space of a vehicle 100 mayinclude environmental sensors, user interface sensor(s), and/or safetysensors. Additionally, or alternatively, there may be sensors associatedwith various devices inside the vehicle (e.g., smart phones, tablets,mobile computers, wearables, etc.)

Environmental sensors may comprise sensors configured to collect datarelating to the internal environment of a vehicle 100. Examples ofenvironmental sensors may include one or more of, but are not limitedto: oxygen/air sensors 301, temperature sensors 303, humidity sensors305, light/photo sensors 307, and more. The oxygen/air sensors 301 maybe configured to detect a quality or characteristic of the air in theinterior space 108 of the vehicle 100 (e.g., ratios and/or types ofgasses comprising the air inside the vehicle 100, dangerous gas levels,safe gas levels, etc.). Temperature sensors 303 may be configured todetect temperature readings of one or more objects, users 216, and/orareas of a vehicle 100. Humidity sensors 305 may detect an amount ofwater vapor present in the air inside the vehicle 100. The light/photosensors 307 can detect an amount of light present in the vehicle 100.Further, the light/photo sensors 307 may be configured to detect variouslevels of light intensity associated with light in the vehicle 100.

User interface sensors may comprise sensors configured to collect datarelating to one or more users (e.g., a driver and/or passenger(s)) in avehicle 100. As can be appreciated, the user interface sensors mayinclude sensors that are configured to collect data from users 216 inone or more areas of the vehicle 100. Examples of user interface sensorsmay include one or more of, but are not limited to: infrared sensors309, motion sensors 311, weight sensors 313, wireless network sensors315, biometric sensors 317, camera (or image) sensors 319, audio sensors321, and more.

Infrared sensors 309 may be used to measure IR light irradiating from atleast one surface, user, or other object in the vehicle 100. Among otherthings, the Infrared sensors 309 may be used to measure temperatures,form images (especially in low light conditions), identify users 216,and even detect motion in the vehicle 100.

The motion sensors 311 may detect motion and/or movement of objectsinside the vehicle 100. Optionally, the motion sensors 311 may be usedalone or in combination to detect movement. For example, a user may beoperating a vehicle 100 (e.g., while driving, etc.) when a passenger inthe rear of the vehicle 100 unbuckles a safety belt and proceeds to moveabout the vehicle 10. In this example, the movement of the passengercould be detected by the motion sensors 311. In response to detectingthe movement and/or the direction associated with the movement, thepassenger may be prevented from interfacing with and/or accessing atleast some of the vehicle control features. As can be appreciated, theuser may be alerted of the movement/motion such that the user can act toprevent the passenger from interfering with the vehicle controls.Optionally, the number of motion sensors in a vehicle may be increasedto increase an accuracy associated with motion detected in the vehicle100.

Weight sensors 313 may be employed to collect data relating to objectsand/or users in various areas of the vehicle 100. In some cases, theweight sensors 313 may be included in the seats and/or floor of avehicle 100. Optionally, the vehicle 100 may include a wireless networksensor 315. This sensor 315 may be configured to detect one or morewireless network(s) inside the vehicle 100. Examples of wirelessnetworks may include, but are not limited to, wireless communicationsutilizing Bluetooth®, Wi-Fi™, ZigBee, IEEE 802.11, and other wirelesstechnology standards. For example, a mobile hotspot may be detectedinside the vehicle 100 via the wireless network sensor 315. In thiscase, the vehicle 100 may determine to utilize and/or share the mobilehotspot detected via/with one or more other devices associated with thevehicle 100.

Biometric sensors 317 may be employed to identify and/or recordcharacteristics associated with a user. It is anticipated that biometricsensors 317 can include at least one of image sensors, IR sensors,fingerprint readers, weight sensors, load cells, force transducers,heart rate monitors, blood pressure monitors, and the like as providedherein.

The camera sensors 319 may record still images, video, and/orcombinations thereof. Camera sensors 319 may be used alone or incombination to identify objects, users, and/or other features, insidethe vehicle 100. Two or more camera sensors 319 may be used incombination to form, among other things, stereo and/or three-dimensional(3D) images. The stereo images can be recorded and/or used to determinedepth associated with objects and/or users in a vehicle 100. Further,the camera sensors 319 used in combination may determine the complexgeometry associated with identifying characteristics of a user. Forexample, the camera sensors 319 may be used to determine dimensionsbetween various features of a user's face (e.g., the depth/distance froma user's nose to a user's cheeks, a linear distance between the centerof a user's eyes, and more). These dimensions may be used to verify,record, and even modify characteristics that serve to identify a user.The camera sensors 319 may also be used to determine movement associatedwith objects and/or users within the vehicle 100. It should beappreciated that the number of image sensors used in a vehicle 100 maybe increased to provide greater dimensional accuracy and/or views of adetected image in the vehicle 100.

The audio sensors 321 may be configured to receive audio input from auser of the vehicle 100. The audio input from a user may correspond tovoice commands, conversations detected in the vehicle 100, phone callsmade in the vehicle 100, and/or other audible expressions made in thevehicle 100. Audio sensors 321 may include, but are not limited to,microphones and other types of acoustic-to-electric transducers orsensors. Optionally, the interior audio sensors 321 may be configured toreceive and convert sound waves into an equivalent analog or digitalsignal. The interior audio sensors 321 may serve to determine one ormore locations associated with various sounds in the vehicle 100. Thelocation of the sounds may be determined based on a comparison of volumelevels, intensity, and the like, between sounds detected by two or moreinterior audio sensors 321. For instance, a first audio sensor 321 maybe located in a first area of the vehicle 100 and a second audio sensor321 may be located in a second area of the vehicle 100. If a sound isdetected at a first volume level by the first audio sensors 321 A and asecond, higher, volume level by the second audio sensors 321 in thesecond area of the vehicle 100, the sound may be determined to be closerto the second area of the vehicle 100. As can be appreciated, the numberof sound receivers used in a vehicle 100 may be increased (e.g., morethan two, etc.) to increase measurement accuracy surrounding sounddetection and location, or source, of the sound (e.g., viatriangulation, etc.).

The safety sensors may comprise sensors configured to collect datarelating to the safety of a user and/or one or more components of avehicle 100. Examples of safety sensors may include one or more of, butare not limited to: force sensors 325, mechanical motion sensors 327,orientation sensors 329, restraint sensors 331, and more.

The force sensors 325 may include one or more sensors inside the vehicle100 configured to detect a force observed in the vehicle 100. Oneexample of a force sensor 325 may include a force transducer thatconverts measured forces (e.g., force, weight, pressure, etc.) intooutput signals. Mechanical motion sensors 327 may correspond toencoders, accelerometers, damped masses, and the like. Optionally, themechanical motion sensors 327 may be adapted to measure the force ofgravity (i.e., G-force) as observed inside the vehicle 100. Measuringthe G-force observed inside a vehicle 100 can provide valuableinformation related to a vehicle's acceleration, deceleration,collisions, and/or forces that may have been suffered by one or moreusers in the vehicle 100. Orientation sensors 329 can includeaccelerometers, gyroscopes, magnetic sensors, and the like that areconfigured to detect an orientation associated with the vehicle 100.

The restraint sensors 331 may correspond to sensors associated with oneor more restraint devices and/or systems in a vehicle 100. Seatbelts andairbags are examples of restraint devices and/or systems. As can beappreciated, the restraint devices and/or systems may be associated withone or more sensors that are configured to detect a state of thedevice/system. The state may include extension, engagement, retraction,disengagement, deployment, and/or other electrical or mechanicalconditions associated with the device/system.

The associated device sensors 323 can include any sensors that areassociated with a device in the vehicle 100. As previously stated,typical devices may include smart phones, tablets, laptops, mobilecomputers, and the like. It is anticipated that the various sensorsassociated with these devices can be employed by the vehicle controlsystem 348. For example, a typical smart phone can include, an imagesensor, an IR sensor, audio sensor, gyroscope, accelerometer, wirelessnetwork sensor, fingerprint reader, and more. It is an aspect of thepresent disclosure that one or more of these associated device sensors323 may be used by one or more subsystems of the vehicle 100.

FIG. 3C illustrates a GPS/Navigation subsystem(s) 302. The navigationsubsystem(s) 302 can be any present or future-built navigation systemthat may use location data, for example, from the Global PositioningSystem (GPS), to provide navigation information or control the vehicle100. The navigation subsystem(s) 302 can include several components,such as, one or more of, but not limited to: a GPS Antenna/receiver 331,a location module 333, a maps database 335, etc. Generally, the severalcomponents or modules 331-335 may be hardware, software, firmware,computer readable media, or combinations thereof.

A GPS Antenna/receiver 331 can be any antenna, GPS puck, and/or receivercapable of receiving signals from a GPS satellite or other navigationsystem. The signals may be demodulated, converted, interpreted, etc. bythe GPS Antenna/receiver 331 and provided to the location module 333.Thus, the GPS Antenna/receiver 331 may convert the time signals from theGPS system and provide a location (e.g., coordinates on a map) to thelocation module 333. Alternatively, the location module 333 caninterpret the time signals into coordinates or other locationinformation.

The location module 333 can be the controller of the satellitenavigation system designed for use in the vehicle 100. The locationmodule 333 can acquire position data, as from the GPS Antenna/receiver331, to locate the user or vehicle 100 on a road in the unit's mapdatabase 335. Using the road database 335, the location module 333 cangive directions to other locations along roads also in the database 335.When a GPS signal is not available, the location module 333 may applydead reckoning to estimate distance data from sensors 304 including oneor more of, but not limited to, a speed sensor attached to the drivetrain of the vehicle 100, a gyroscope, an accelerometer, etc.Additionally, or alternatively, the location module 333 may use knownlocations of Wi-Fi hotspots, cell tower data, etc. to determine theposition of the vehicle 100, such as by using Time Difference Of Arrival(TDOA) and/or Frequency Difference Of Arrival (FDOA) techniques.

The maps database 335 can include any hardware and/or software to storeinformation about maps, Geographical Information System (GIS)information, location information, etc. The maps database 335 caninclude any data definition or other structure to store the information.Generally, the maps database 335 can include a road database that mayinclude one or more vector maps of areas of interest. Street names,street numbers, house numbers, and other information can be encoded asgeographic coordinates so that the user can find some desireddestination by street address. Points of interest (waypoints) can alsobe stored with their geographic coordinates. For example, a point ofinterest may include speed cameras, fuel stations, public parking, and“parked here” (or “you parked here”) information. The maps database 335may also include road or street characteristics, for example, speedlimits, location of stop lights/stop signs, lane divisions, schoollocations, etc. The map database contents can be produced or updated bya server connected through a wireless system in communication with theInternet, even as the vehicle 100 is driven along existing streets,yielding an up-to-date map.

The vehicle control system 348, when operating in L4 or L5 and based onsensor information from the external and interior vehicle sensors, cancontrol the driving behavior of the vehicle in response to the currentvehicle location, sensed object information, sensed vehicle occupantinformation, vehicle-related information, exterior environmentalinformation, and navigation information from the maps database 335.

The sensed object information refers to sensed information regardingobjects external to the vehicle. Examples include animate objects suchas animals and attributes thereof (e.g., animal type, current spatiallocation, current activity, etc.), and pedestrians and attributesthereof (e.g., identity, age, sex, current spatial location, currentactivity, etc.), and the like and inanimate objects and attributesthereof such as other vehicles (e.g., current vehicle state or activity(parked or in motion or level of automation currently employed),occupant or operator identity, vehicle type (truck, car, etc.), vehiclespatial location, etc.), curbs (topography and spatial location),potholes (size and spatial location), lane division markers (type orcolor and spatial locations), signage (type or color and spatiallocations such as speed limit signs, yield signs, stop signs, and otherrestrictive or warning signs), traffic signals (e.g., red, yellow, blue,green, etc.), buildings (spatial locations), walls (height and spatiallocations), barricades (height and spatial location), and the like.

The sensed occupant information refers to sensed information regardingoccupants internal to the vehicle. Examples include the number andidentities of occupants and attributes thereof (e.g., seating position,age, sex, gaze direction, biometric information, authenticationinformation, preferences, historic behavior patterns (such as current orhistorical user driving behavior, historical user route, destination,and waypoint preferences), nationality, ethnicity and race, languagepreferences (e.g., Spanish, English, Chinese, etc.), current occupantrole (e.g., operator or passenger), occupant priority ranking (e.g.,vehicle owner is given a higher ranking than a child occupant),electronic calendar information (e.g., Outlook™), and medicalinformation and history, etc.

The vehicle-related information refers to sensed information regardingthe selected vehicle. Examples include vehicle manufacturer, type,model, year of manufacture, current geographic location, current vehiclestate or activity (parked or in motion or level of automation currentlyemployed), vehicle specifications and capabilities, currently sensedoperational parameters for the vehicle, and other information.

The exterior environmental information refers to sensed informationregarding the external environment of the selected vehicle. Examplesinclude road type (pavement, gravel, brick, etc.), road condition (e.g.,wet, dry, icy, snowy, etc.), weather condition (e.g., outsidetemperature, pressure, humidity, wind speed and direction, etc.),ambient light conditions (e.g., time-of-day), degree of development ofvehicle surroundings (e.g., urban or rural), and the like.

In a typical implementation, the automated vehicle control system 348,based on feedback from certain sensors, specifically the LIDAR and radarsensors positioned around the circumference of the vehicle, constructs athree-dimensional map in spatial proximity to the vehicle that enablesthe automated vehicle control system 348 to identify and spatiallylocate animate and inanimate objects. Other sensors, such as inertialmeasurement units, gyroscopes, wheel encoders, sonar sensors, motionsensors to perform odometry calculations with respect to nearby movingexterior objects, and exterior facing cameras (e.g., to perform computervision processing) can provide further contextual information forgeneration of a more accurate three-dimensional map. The navigationinformation is combined with the three-dimensional map to provide short,intermediate and long-range course tracking and route selection. Thevehicle control system 348 processes real-world information as well asGPS data, and driving speed to determine accurately the precise positionof each vehicle, down to a few centimeters all while making correctionsfor nearby animate and inanimate objects.

The vehicle control system 348 can process in substantial real time theaggregate mapping information and models (or predicts) behavior ofoccupants of the current vehicle and other nearby animate or inanimateobjects and, based on the aggregate mapping information and modeledbehavior, issues appropriate commands regarding vehicle operation. Whilesome commands are hard-coded into the vehicle, such as stopping at redlights and stop signs, other responses are learned and recorded byprofile updates based on previous driving experiences. Examples oflearned behavior include a slow-moving or stopped vehicle or emergencyvehicle in a right lane suggests a higher probability that the carfollowing it will attempt to pass, a pot hole, rock, or other foreignobject in the roadway equates to a higher probability that a driver willswerve to avoid it, and traffic congestion in one lane means that otherdrivers moving in the same direction will have a higher probability ofpassing in an adjacent lane or by driving on the shoulder.

FIG. 4 shows one embodiment of the instrument panel 400 of the vehicle100. The instrument panel 400 of vehicle 100 comprises a steering wheel410, a vehicle operational display 420 (e.g., configured to presentand/or display driving data such as speed, measured air resistance,vehicle information, entertainment information, etc.), one or moreauxiliary displays 424 (e.g., configured to present and/or displayinformation segregated from the operational display 420, entertainmentapplications, movies, music, etc.), a heads-up display 434 (e.g.,configured to display any information previously described including,but in no way limited to, guidance information such as route todestination, or obstacle warning information to warn of a potentialcollision, or some or all primary vehicle operational data such asspeed, resistance, etc.), a power management display 428 (e.g.,configured to display data corresponding to electric power levels ofvehicle 100, reserve power, charging status, etc.), and an input device432 (e.g., a controller, touchscreen, or other interface deviceconfigured to interface with one or more displays in the instrumentpanel or components of the vehicle 100. The input device 432 may beconfigured as a joystick, mouse, touchpad, tablet, 3D gesture capturedevice, etc.). In some embodiments, the input device 432 may be used tomanually maneuver a portion of the vehicle 100 into a charging position(e.g., moving a charging plate to a desired separation distance, etc.).

While one or more of displays of instrument panel 400 may betouch-screen displays, it should be appreciated that the vehicleoperational display may be a display incapable of receiving touch input.For instance, the operational display 420 that spans across an interiorspace centerline 404 and across both a first zone 408A and a second zone408B may be isolated from receiving input from touch, especially from apassenger. In some cases, a display that provides vehicle operation orcritical systems information and interface may be restricted fromreceiving touch input and/or be configured as a non-touch display. Thistype of configuration can prevent dangerous mistakes in providing touchinput where such input may cause an accident or unwanted control.

In some embodiments, one or more displays of the instrument panel 400may be mobile devices and/or applications residing on a mobile devicesuch as a smart phone. Additionally, or alternatively, any of theinformation described herein may be presented to one or more portions420A-N of the operational display 420 or other display 424, 428, 434. Inone embodiment, one or more displays of the instrument panel 400 may bephysically separated or detached from the instrument panel 400. In somecases, a detachable display may remain tethered to the instrument panel.

The portions 420A-N of the operational display 420 may be dynamicallyreconfigured and/or resized to suit any display of information asdescribed. Additionally, or alternatively, the number of portions 420A-Nused to visually present information via the operational display 420 maybe dynamically increased or decreased as required, and are not limitedto the configurations shown.

FIG. 5 illustrates a hardware diagram of communications componentry thatcan be optionally associated with the vehicle 100 in accordance withembodiments of the present disclosure.

The communications componentry can include one or more wired or wirelessdevices such as a transceiver(s) and/or modem that allows communicationsnot only between the various systems disclosed herein but also withother devices, such as devices on a network, and/or on a distributednetwork such as the Internet and/or in the cloud and/or with anothervehicle(s).

The communications subsystem 350 can also include inter- andintra-vehicle communications capabilities such as hotspot and/or accesspoint connectivity for any one or more of the vehicle occupants and/orvehicle-to-vehicle communications.

Additionally, and while not specifically illustrated, the communicationssubsystem 350 can include one or more communications links (that can bewired or wireless) and/or communications busses (managed by the busmanager 574), including one or more of Controller Area Network (CAN)bus, OBD-II, ARCINC 429, Byteflight, Domestic Digital Bus (D2B),FlexRay, DC-BUS, IDB-1394, IEBus, I2C, ISO 9141-1/-2, J1708, J1587,J1850, J1939, ISO 11783, Keyword Protocol 2000, Local InterconnectNetwork (LIN), Media Oriented Systems Transport (MOST), MultifunctionVehicle Bus, SMARTwireX, SPI, Vehicle Area Network (VAN), and the likeor in general any communications protocol and/or standard(s).

The various protocols and communications can be communicated one or moreof wirelessly and/or over transmission media such as single wire,twisted pair, fiber optic, IEEE 1394, MIL-STD-1553, MIL-STD-1773,power-line communication, or the like. (All of the above standards andprotocols are incorporated herein by reference in their entirety).

As discussed, the communications subsystem 350 enables communicationsbetween any of the inter-vehicle systems and subsystems as well ascommunications with non-collocated resources, such as those reachableover a network such as the Internet.

The communications subsystem 350, in addition to well-known componentry(which has been omitted for clarity), includes interconnected elementsincluding one or more of: one or more antennas 504, aninterleaver/deinterleaver 508, an analog front end (AFE) 512,memory/storage/cache 516, controller/microprocessor 520, MAC circuitry522, modulator/demodulator 524, encoder/decoder 528, a plurality ofconnectivity managers 534, 558, 562, 566, GPU 540, accelerator 544, amultiplexer/demultiplexer 552, transmitter 570, receiver 572 andadditional wireless radio components such as a Wi-Fi PHY/Bluetooth®module 580, a Wi-Fi/BT MAC module 584, additional transmitter(s) 588 andadditional receiver(s) 592. The various elements in the device 350 areconnected by one or more links/busses 5 (not shown, again for sake ofclarity).

The device 350 can have one more antennas 504, for use in wirelesscommunications such as Multi-Input Multi-Output (MIMO) communications,Multi-User Multi-Input Multi-Output (MU-MIMO) communications Bluetooth®,LTE, 4G, 5G, Near-Field Communication (NFC), etc., and in general forany type of wireless communications. The antenna(s) 504 can include, butare not limited to one or more of directional antennas, omnidirectionalantennas, monopoles, patch antennas, loop antennas, microstrip antennas,dipoles, and any other antenna(s) suitable for communicationtransmission/reception. In an exemplary embodiment,transmission/reception using MIMO may require particular antennaspacing. In another exemplary embodiment, MIMO transmission/receptioncan enable spatial diversity allowing for different channelcharacteristics at each of the antennas. In yet another embodiment, MIMOtransmission/reception can be used to distribute resources to multipleusers for example within the vehicle 100 and/or in another vehicle.

Antenna(s) 504 generally interact with the Analog Front End (AFE) 512,which is needed to enable the correct processing of the receivedmodulated signal and signal conditioning for a transmitted signal. TheAFE 512 can be functionally located between the antenna and a digitalbaseband system to convert the analog signal into a digital signal forprocessing and vice-versa.

The subsystem 350 can also include a controller/microprocessor 520 and amemory/storage/cache 516. The subsystem 350 can interact with thememory/storage/cache 516 which may store information and operationsnecessary for configuring and transmitting or receiving the informationdescribed herein. The memory/storage/cache 516 may also be used inconnection with the execution of application programming or instructionsby the controller/microprocessor 520, and for temporary or long-termstorage of program instructions and/or data. As examples, thememory/storage/cache 520 may comprise a computer-readable device, RAM,ROM, DRAM, SDRAM, and/or other storage device(s) and media.

The controller/microprocessor 520 may comprise a general-purposeprogrammable processor or controller for executing applicationprogramming or instructions related to the subsystem 350. Furthermore,the controller/microprocessor 520 can perform operations for configuringand transmitting/receiving information as described herein. Thecontroller/microprocessor 520 may include multiple processor cores,and/or implement multiple virtual processors. Optionally, thecontroller/microprocessor 520 may include multiple physical processors.By way of example, the controller/microprocessor 520 may comprise aspecially configured Application Specific Integrated Circuit (ASIC) orother integrated circuit, a digital signal processor(s), a controller, ahardwired electronic or logic circuit, a programmable logic device orgate array, a special purpose computer, or the like.

The subsystem 350 can further include a transmitter(s) 570, 588 andreceiver(s) 572, 592 which can transmit and receive signals,respectively, to and from other devices, subsystems and/or otherdestinations using the one or more antennas 504 and/or links/busses.Included in the subsystem 350 circuitry is the medium access control orMAC Circuitry 522. MAC circuitry 522 provides for controlling access tothe wireless medium. In an exemplary embodiment, the MAC circuitry 522may be arranged to contend for the wireless medium and configure framesor packets for communicating over the wired/wireless medium.

The subsystem 350 can also optionally contain a security module (notshown). This security module can contain information regarding but notlimited to, security parameters required to connect the device to one ormore other devices or other available network(s), and can include WEP orWPA/WPA-2 (optionally+AES and/or TKIP) security access keys, networkkeys, etc. The WEP security access key is a security password used byWi-Fi networks. Knowledge of this code can enable a wireless device toexchange information with an access point and/or another device. Theinformation exchange can occur through encoded messages with the WEPaccess code often being chosen by the network administrator. WPA is anadded security standard that is also used in conjunction with networkconnectivity with stronger encryption than WEP.

In some embodiments, the communications subsystem 350 also includes aGPU 540, an accelerator 544, a Wi-Fi/BT/BLE (Bluetooth® Low-Energy) PHYmodule 580 and a Wi-Fi/BT/BLE MAC module 584 and optional wirelesstransmitter 588 and optional wireless receiver 592. In some embodiments,the GPU 540 may be a graphics processing unit, or visual processingunit, comprising at least one circuit and/or chip that manipulates andchanges memory to accelerate the creation of images in a frame bufferfor output to at least one display device. The GPU 540 may include oneor more of a display device connection port, Printed Circuit Board(PCB), a GPU chip, a Metal-Oxide-Semiconductor Field-Effect Transistor(MOSFET), memory (e.g., Single Data rate Random-Access Memory (SDRAM),Double Data Rate Random-Access Memory (DDR RAM), etc., and/orcombinations thereof), a secondary processing chip (e.g., handling videoout capabilities, processing, and/or other functions in addition to theGPU chip, etc.), a capacitor, heatsink, temperature control or coolingfan, motherboard connection, shielding, and the like.

The various connectivity managers 534, 558, 562, 566 manage and/orcoordinate communications between the subsystem 350 and one or more ofthe systems disclosed herein and one or more other devices/systems. Theconnectivity managers 534, 558, 562, 566 include a charging connectivitymanager 534, a vehicle database connectivity manager 558, a remoteoperating system connectivity manager 562, and a sensor connectivitymanager 566.

The charging connectivity manager 534 can coordinate not only thephysical connectivity between the vehicle 100 and a chargingdevice/vehicle, but can also communicate with one or more of a powermanagement controller, one or more third parties and optionally abilling system(s). As an example, the vehicle 100 can establishcommunications with the charging device/vehicle to one or more ofcoordinate interconnectivity between the two (e.g., by spatiallyaligning the charging receptacle on the vehicle with the charger on thecharging vehicle) and optionally share navigation information. Oncecharging is complete, the amount of charge provided can be tracked andoptionally forwarded to, for example, a third party for billing. Inaddition to being able to manage connectivity for the exchange of power,the charging connectivity manager 534 can also communicate information,such as billing information to the charging vehicle and/or a thirdparty. This billing information could be, for example, the owner of thevehicle, the driver/occupant(s) of the vehicle, company information, orin general any information usable to charge the appropriate entity forthe power received.

The vehicle database connectivity manager 558 allows the subsystem toreceive and/or share information stored in the vehicle database. Thisinformation can be shared with other vehicle components/subsystemsand/or other entities, such as third parties and/or charging systems.The information can also be shared with one or more vehicle occupantdevices, such as an app (application) on a mobile device the driver usesto track information about the vehicle 100 and/or a dealer orservice/maintenance provider. In general, any information stored in thevehicle database can optionally be shared with any one or more otherdevices optionally subject to any privacy or confidentiallyrestrictions.

The remote operating system connectivity manager 562 facilitatescommunications between the vehicle 100 and any one or more autonomousvehicle systems. These communications can include one or more ofnavigation information, vehicle information, other vehicle information,weather information, occupant information, or in general any informationrelated to the remote operation of the vehicle 100.

The sensor connectivity manager 566 facilitates communications betweenany one or more of the vehicle sensors (e.g., the driving vehiclesensors and systems 304, etc.) and any one or more of the other vehiclesystems. The sensor connectivity manager 566 can also facilitatecommunications between any one or more of the sensors and/or vehiclesystems and any other destination, such as a service company, app, or ingeneral to any destination where sensor data is needed.

In accordance with one exemplary embodiment, any of the communicationsdiscussed herein can be communicated via the conductor(s) used forcharging. One exemplary protocol usable for these communications isPower-Line Communication (PLC). PLC is a communication protocol thatuses electrical wiring to simultaneously carry both data, andAlternating Current (AC) electric power transmission or electric powerdistribution. It is also known as power-line carrier, Power-line DigitalSubscriber Line (PDSL), mains communication, power-linetelecommunications, or Power-Line Networking (PLN). For DC environmentsin vehicles PLC can be used in conjunction with CAN-bus, LIN-bus overpower line (DC-LIN) and DC-BUS.

The communications subsystem can also optionally manage one or moreidentifiers, such as an Internet Protocol (IP) address(es), associatedwith the vehicle and one or other system or subsystems or componentsand/or devices therein. These identifiers can be used in conjunctionwith any one or more of the connectivity managers as discussed herein.

FIG. 6 illustrates a block diagram of a computing environment 600 thatmay function as the servers, user computers, or other systems providedand described herein. The computing environment 600 includes one or moreuser computers, or computing devices, such as a vehicle computing device604, a communication device 608, and/or more 612. The computing devices604, 608, 612 may include general purpose personal computers (including,merely by way of example, personal computers, and/or laptop computersrunning various versions of Microsoft Corp.'s Windows® and/or AppleCorp.'s Macintosh® operating systems) and/or workstation computersrunning any of a variety of commercially-available UNIX® or UNIX-likeoperating systems. These computing devices 604, 608, 612 may also haveany of a variety of applications, including for example, database clientand/or server applications, and web browser applications. Alternatively,the computing devices 604, 608, 612 may be any other electronic device,such as a thin-client computer, Internet-enabled mobile telephone,and/or personal digital assistant, capable of communicating via anetwork 352 and/or displaying and navigating web pages or other types ofelectronic documents or information. Although the exemplary computingenvironment 600 is shown with two computing devices, any number of usercomputers or computing devices may be supported.

The computing environment 600 may also include one or more servers 614,616. In this example, server 614 is shown as a web server and server 616is shown as an application server. The web server 614, which may be usedto process requests for web pages or other electronic documents fromcomputing devices 604, 608, 612. The web server 614 can be running anoperating system including any of those discussed above, as well as anycommercially-available server operating systems. The web server 614 canalso run a variety of server applications, including Session InitiationProtocol (SIP) servers, HyperText Transport Protocol (secure) (HTTP(s))servers, File Transfer Protocol (FTP) servers, Common Gateway Interface(CGI) servers, database servers, Java® servers, and the like. In someinstances, the web server 614 may publish operations availableoperations as one or more web services.

The computing environment 600 may also include one or more file andor/application servers 616, which can, in addition to an operatingsystem, include one or more applications accessible by a client runningon one or more of the computing devices 604, 608, 612. The server(s) 616and/or 614 may be one or more general purpose computers capable ofexecuting programs or scripts in response to the computing devices 604,608, 612. As one example, the server 616, 614 may execute one or moreweb applications. The web application may be implemented as one or morescripts or programs written in any programming language, such as Java®,C, C#®, or C++, and/or any scripting language, such as Perl, Python, orTCL, as well as combinations of any programming/scripting languages. Theapplication server(s) 616 may also include database servers, includingwithout limitation those commercially available from Oracle®,Microsoft®, Sybase®, IBM® and the like, which can process requests fromdatabase clients running on a computing device 604, 608, 612.

The web pages created by the server 614 and/or 616 may be forwarded to acomputing device 604, 608, 612 via a web (file) server 614, 616.Similarly, the web server 614 may be able to receive web page requests,web services invocations, and/or input data from a computing device 604,608, 612 (e.g., a user computer, etc.) and can forward the web pagerequests and/or input data to the web (application) server 616. Infurther embodiments, the server 616 may function as a file server.Although for ease of description, FIG. 6 illustrates a separate webserver 614 and file/application server 616, those skilled in the artwill recognize that the functions described with respect to servers 614,616 may be performed by a single server and/or a plurality ofspecialized servers, depending on implementation-specific needs andparameters. The computer systems 604, 608, 612, web (file) server 614and/or web (application) server 616 may function as the system, devices,or components described in FIGS. 1-6.

The computing environment 600 may also include a database 618. Thedatabase 618 may reside in a variety of locations. By way of example,database 618 may reside on a storage medium local to (and/or residentin) one or more of the computers 604, 608, 612, 614, 616. Alternatively,it may be remote from any or all of the computers 604, 608, 612, 614,616, and in communication (e.g., via the network 352) with one or moreof these. The database 618 may reside in a Storage-Area Network (SAN)familiar to those skilled in the art. Similarly, any necessary files forperforming the functions attributed to the computers 604, 608, 612, 614,616 may be stored locally on the respective computer and/or remotely, asappropriate. The database 618 may be a relational database, such asOracle 20i®, that is adapted to store, update, and retrieve data inresponse to SQL-formatted commands.

FIG. 7 illustrates one embodiment of a computer system 700 upon whichthe servers, user computers, computing devices, or other systems orcomponents described above may be deployed or executed. The computersystem 700 is shown comprising hardware elements that may beelectrically coupled via a bus 704. The hardware elements may includeone or more central processing units (CPUs) 708; one or more inputdevices 712 (e.g., a mouse, a keyboard, etc.); and one or more outputdevices 716 (e.g., a display device, a printer, etc.). The computersystem 700 may also include one or more storage devices 720. By way ofexample, storage device(s) 720 may be disk drives, optical storagedevices, solid-state storage devices such as a Random-Access Memory(RAM) and/or a Read-Only Memory (ROM), which can be programmable,flash-updateable and/or the like.

The computer system 700 may additionally include a computer-readablestorage media reader 724; a communications system 728 (e.g., a modem, anetwork card (wireless or wired), an infra-red communication device,etc.); and working memory 736, which may include RAM and ROM devices asdescribed above. The computer system 700 may also include a processingacceleration unit 732, which can include a DSP, a special-purposeprocessor, and/or the like.

The computer-readable storage media reader 724 can further be connectedto a computer-readable storage medium, together (and, optionally, incombination with storage device(s) 720) comprehensively representingremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containingcomputer-readable information. The communications system 728 may permitdata to be exchanged with a network and/or any other computer describedabove with respect to the computer environments described herein.Moreover, as disclosed herein, the term “storage medium” may representone or more devices for storing data, including Read-Only Memory (ROM),Random Access Memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine-readable mediums for storing information.

The computer system 700 may also comprise software elements, shown asbeing currently located within a working memory 736, including anoperating system 740 and/or other code 744. It should be appreciatedthat alternate embodiments of a computer system 700 may have numerousvariations from that described above. For example, customized hardwaremight also be used and/or particular elements might be implemented inhardware, software (including portable software, such as applets), orboth. Further, connection to other computing devices such as networkinput/output devices may be employed.

Examples of the processors 340, 708 as described herein may include, butare not limited to, at least one of Qualcomm® Snapdragon® 800 and 801,Qualcomm® Snapdragon® 620 and 615 with 4G LTE Integration and 64-bitcomputing, Apple® A7 processor with 64-bit architecture, Apple® M7motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family ofprocessors, the Intel® Xeon® family of processors, the Intel® Atom™family of processors, the Intel Itanium® family of processors, Intel®Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nmIvy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300,and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments®Jacinto C6000™ automotive infotainment processors, Texas Instruments®OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors,ARM® Cortex-A and ARM926EJ-S™ processors, other industry-equivalentprocessors, and may perform computational functions using any known orfuture-developed standard, instruction set, libraries, and/orarchitecture.

Visualizations of embodiments for both segmenting routes and for usingfeature vectors for location identification may be as shown in FIGS.8A-8H. In FIG. 8A, a visualization 800 of a digital may represent avehicle 100 be traveling between an origination point 804 and adestination 812. The vehicle 100 may travel along a route 816. Fordriverless vehicles and for location with GPS systems or other types ofnavigation systems, the vehicle 100 may use GPS to determine a generallocation 820 of the vehicle 100. Additionally, or alternatively, thegeneral location 820 may be determined by dead reckoning, visualodometry, particle filtering, and/or another process.

Dead reckoning may be the process of calculating the vehicle's positionby estimating the direction and distance traveled rather than by usinglandmarks, astronomical observations, or electronic navigation methods.Visual odometry is related to dead reckoning in that, in computer visionsystems (for example, the autonomous vehicle), visual odometry is theprocess of determining the position and orientation of the vehicle byanalyzing the associated camera images. The analysis of a consecutive ora sequence of camera images, in visual odometry, can estimate thedirection and distance traveled that is used for dead reckoning.Particle filtering can be the process of weighting and eliminating roadsegments, and therefore, which feature vectors, may be associated withthe vehicle's position. A feature vector can be any type ofvisualization of an environment around a vehicle 100 at a segment 824.The feature vector may be comprised of observations of machine view ofstructures or static targets in the environment. The feature vector mayinclude the outlines or partial outlines of the targets, may include asingle target or multiple targets, etc. Using the process above, thevehicle 100 can weight road segments as more likely to be the exactlocation of the vehicle 100 if those road segments are nearer thedetermined position derived from GPS or dead reckoning.

Due to vagaries in signal accuracy of the GPS signal, due tointerference of the GPS signal, or due to the inexactness of the deadreckoning, visual odometry, particle filtering processes above, theexact location of the vehicle 100 may be anywhere within some arearepresented by box 820. For example, the vehicle 100 may be within oneto three meters of the vehicle's actual exact location. As such, withoutdetermining the exact position, the navigation system of the vehicle 100may locate the vehicle 100 in an incorrect position (e.g., outside alane or off a road surface) when the vehicle 100 may actually be drivingdown the middle of the lane or road. Due to these errors orinaccuracies, the vehicle 100 may not rely GPS for driverless orautonomous operation, but requires several other sensors to keep thevehicle 100 in the correct position on the road.

In a representation 822 of embodiments presented herein, the vehicle 100may be traveling down a route 816 as shown FIG. 8B. This route 816 maybe broken into or represented by two or more sections or segments 824a-824 h. Each segment 824 of the route 816 may have associated therewithdata, such as feature vector data.

The segment 824 can be located at the middle of the lane, and thus, whenthe vehicle 100 determines the nearest segment 824, the vehicle 100 canassume the vehicle's position is in the middle of the lane or thevehicle 100 can determine an error (how far from the vehicle is) fromthe middle of the lane or from the segment 824. This error from one ormore segments 824 can be based on differences in the feature vectorsdescribed hereinafter.

The segments may have any type of frequency or separation. For example,the segment 824 a may be a millimeter, an inch, a foot, etc. fromsegment 824 b. As such, the vehicle 100 can have a more granulated ormore precise location understanding based on a higher number of segments824 per route 816.

In other embodiments, the route or the surface of the road 828 may bedesignated by a series of segments 824 spread throughout the area 828.In this way, the vehicle 100 can know where the vehicle is insubstantially all of the road surface 828 rather than just at or nearthe middle of the road as shown with feature vectors 824 a-824 h.Regardless of how the segments 824, 828 are positioned within the route816, the route 816 can have segments 824 associated therewith, and eachof the segments 824 may have data associated therewith describing thesegment's position and other information associated therewith asdescribed in conjunction with FIGS. 9B-9C.

FIGS. 8C-8E show schematic views of machine vision or imaging sensorinformation 832, 836, 840, detected by at least one imaging system ofthe vehicle 100, describing a visual environment (e.g., at some point ator around the vehicle 100) that changes over time (e.g., while thevehicle 100 is driving, etc.). In some circumstances, the imaging systemmay be one or more of the imaging sensors 116A, 116F, 332, 336 (e.g., acamera, etc.) described above. The schematic views of FIGS. 8C-8E showcomputer-generated images 832, 836, 840 including one or more targets844A-E that are detected as changing in shape, size, range, and/orgeometry while the vehicle 100 is operating along a path 848 or roadway.

FIG. 8C shows a schematic view of imaging sensor information 166detected by the imaging system of the vehicle 100 at a first time oftravel T1 in accordance with embodiments of the present disclosure. Thevehicle 100 may be driving down a street, roadway, or other driving path848. As the vehicle 100 is driving, the imaging system 116 may visuallydetect targets 844A-E in a sensing area of the imaging system 116describing (e.g., visually) an environment outside of the vehicle 100.The environment may include a first target 844A (e.g., a roadway edge, astreet curb, another vehicle, a pedestrian, an object, etc.) on theroadway 848, and/or one or more other targets 844B-E (e.g., buildings,landmarks, signs, markers, etc.).

As the vehicle 100 moves along the path 848, visual characteristicsassociated with the targets 844A-E may change at a second time T2. FIG.8D shows a schematic view of imaging sensor information 802 detected bythe imaging system 116 of the vehicle 100 at a second time of travel T2in accordance with embodiments of the present disclosure. In FIG. 8D,the range to all of the targets 844A-E has changed. For example, thesize and shape of the building targets 844B, 844D, 844E have increasedin dimension at the second time T2, while building target 844C is shownmoving off-image.

As the vehicle 100 continues to move along the path 848 at a subsequenttime, the visual characteristics associated with the targets 844A-E maycontinue to change. In FIG. 8E, a schematic view of imaging sensorinformation detected by the imaging system 116 of the vehicle 100 at athird time of travel T3 is shown in accordance with embodiments of thepresent disclosure. FIG. 8E shows that target information has changed toinclude a larger shape and size associated with some targets, whileother targets have moved completely off-image. For instance, thebuilding target 844B has increased in shape and size, while buildingtarget 844D is shown moving off-image and building targets 844C, 844Ehave moved completely off-image. In some embodiments, FIGS. 8C-8E showimaging sensor information changing over time (e.g., T1-T3) which can bethe basis for visual odometry.

In view 832, a vehicle 100 may at a first road segment, for example,segment 824A, and may view buildings and other structures through one ormore image sensors 116 or other sensors 304 at time T1. Thevisualization 832 can include one or more building targets or structures844B-844E. Each of these structures 844B-844E can provide for atwo-dimensional or 3D image associated with the segment 824. The segment824 may be then positioned within a roadway 848 to determine the localposition of the vehicle.

When the vehicle moves to a next segment as shown in view 836, themachine view 836 may change with buildings 844B-844E in differentpositions. By matching the visualization 832, 836 with storedinformation about each segment, the vehicle 100 can determine where onroadway 848 the vehicle 100 is currently located. If the views 832 and836 do not necessarily match completely with the stored feature vectorsassociated with the segment 824, the vehicle 100 may extrapolate aposition based on the vehicle's location or the locations of structures844B-844E in comparison to the data stored in the vehicle database. At afurther segment 824 arrived at time T3, shown in view 840, some of thebuildings have disappeared and only buildings 844B and 844D are present.Thus, the vehicle 100 is further along the roadway 848. While buildingsare shown as being used for characteristic localization, other types ofstructures or physical things may be used. For example, doorways, trees,fire hydrants, windows, street curbs, street lights, stop signs, etc.can be used to determine where a vehicle 100 is located or what may beviewed by vehicle sensors at a certain segment 824.

Another view 832 of roadway 848 may be as shown in FIG. 8F. In someconfigurations, the complete outlines or visualizations of the targets(e.g., buildings 844) are not used for localization. Rather, thefeatures may comprise only portions of the targets 844 that are used todetermine a location for the vehicle 100. For example, each building 844may be broken into certain structures or elements that may be viewed bythe vehicle 100. For example, feature vectors 852A-852F may representonly the corners of the buildings 844. By storing information orcharacteristics about these features 852, the vehicle 100 does not needto match up a complete machine visualization of all items within theview 832 to a stored image to determine a vehicle position. Rather, eachfeature vector 852 may only describe one or more portions of one or moretargets 844 associated with the segment 824. Further, just essentialdata may be stored about each feature vector to determine thelocalization. The feature vector information may be as described inconjunction with FIG. 9B.

As shown in FIGS. 8G and 8H, the vehicle 100 can use differentinformation in view 832 than that shown in FIG. 8D or 8E. At a nextsegment 824, shown in FIG. 8G, the superfluous information from view 832is extracted in view 836. For example, in comparing FIG. 8D with FIG.8G, the feature vectors 852, shown in FIG. 8G, eliminate extraneousinformation not used for the feature vectors, as shown in FIG. 8D. Inthis way, the feature vectors 852 become easier to identify and containless information overall than providing information about the entireview 836.

In still a further segment 824, shown in FIG. 8H which is a derivationof the view 840 in FIG. 8E, the feature vectors 852 are shown withoutthe representative circles. Thus, feature vector 852C, 852G, 8852H,contain all information of the view used to localize or identify theposition of the vehicle 100 at the segment 824 at the locale on the road848. In this way, the view 840 provides a much smaller data set for thefeature vector(s) that can be stored locally in the vehicle 100 and usedto identify the locale of the vehicle 100.

To accomplish the localization, the vehicle 100 can attempt to match theview of feature vector(s) 852 to information previously stored aboutfeature vectors inside a database. An exact match can provide for theexact location of the vehicle 100 at the segment 824. As the vehicle 100continues to travel on the route 816 and goes to the next segment 824,each segment 824 can be matched up by the feature vectors 852 stored forthe next segment 824 in the vehicle 100, and thus the vehicle 100 candeduce the location of the vehicle 100. In this way, the vehicle 100 candetermine a much more exact location compared to using GPS or by usingother processes described above.

An embodiment of data store 356 for the navigation source containinggeographic information system information 904 may be as shown in FIG.9A. This GIS information 904 stores the different segments of routes orroadways combined with feature vector information. An embodiment of someof the information in the GIS information 904 may be as shown in FIG.9B.

A data structure 908, which contains segment information for a route orroadway section, may be as shown in FIG. 9B. The data structure 908 caninclude one or more of, but is not limited to, a segment identifier (ID)912, a latitude 916, a longitude 920, GIS information 924, featureinformation or feature characteristics 928A-928N, and/or metadata 932.There may be one or more features 852 associated with the segment 824and there may be more or fewer feature information fields 928 than thoseshown in FIG. 9B, as represented by ellipsis 938. There also may be moreor fewer fields than those shown in data structure 908, as representedby ellipsis 936. For each segment 824 a-824 h there may a different datastructure 908 and thus there may be more data structures 908 than thoseshown in FIG. 9B, as represented by ellipsis 940.

The segment identifier 912 can be any type of identifier including analphanumeric identifier, a numeric identifier, a globally uniqueidentifier (GUID), or other types of identifiers. The segment ID 912uniquely identifies the segment 824 compared to all other segments 824in the GIS database 904. As such, the segment ID 912 may be a longseries (e.g., over ten digits) of numbers, letters, and/or other symbolsthat provide for a greater number of different segments 824 (e.g.,1×10¹⁵ identifiers) as the road structure can be broken into manysegments 824.

The latitude 916 and longitude 920 provide the location of the segment824 identified by segment ID 912. The latitude 916 and longitude 920 canbe provided in degrees, minutes, seconds, and tenths, hundredths, etc.of seconds, that provide for the location of the segment.

The GIS information 924 can provide any information beyond just thelatitude and longitude information 916, 920. GIS information 924 caninclude elevations, street names, addresses, etc. This GIS information924 may also identify the location of the segment, but may do so, insome configurations, in a broader sense rather than the narrower oraccurate latitude and longitude 916, 920.

Feature vectors 928 are any information used to describe the feature852, as described in conjunction with FIGS. 8A-8H. The feature vector928 can be a location of a feature using latitude, longitude, other GISinformation, viewing angle, altitude, etc. Further, the feature vector928 can describe what the feature looks like or where the feature 852 islocated in the view as shown in FIG. 8H. This feature vector information928 can provide a description of angles, types of lines, and also anorientation or arrange of the feature 852 in association with otherfeatures 852 within a view 840. Further, the feature 928 may include apointer or other information to other features which should be in theview 840 and can include information about how those features 852 areassociated therewith by distances between features 852, angles from aset plane to another feature 852, viewing relationships, any type ofgrid information for the view 840 or other information. This informationallows the vehicle to match the feature vector information 928 to a viewas described previously in FIGS. 8A-8H. Further, the feature vectorinformation 928 can include a previously created machine view image ofthe features 852 associated with the segment 824 to be visually comparedby the vehicle 100. An example of feature vector information 928 may beas shown in FIG. 9C.

Metadata 932 can be any information related to the segment datastructure 908 or the data therein. Further, metadata 932 can alsoinclude other information not described herein, but associated withother segment information or needed for the processes described herein.For example, the metadata 932 can include update information of when thefeature vectors 928 were updated, the order in which the feature vectors928 were added to the view for that segment 908, or other informationregarding the data structure 908.

An embodiment of a feature vector data structure 928 may be as shown inFIG. 9C. The data structure 928 may include fields for one or more of,but not limited to, a feature vector ID 980, a latitude 916, a longitude920, GIS information 928, appearance characteristics 984, and/or a viewposition 986. There may be more or fewer fields than those shown in FIG.9C, as represented by ellipsis 992. Each feature vector 928, asdescribed in conjunction with segment information 908, may have adifferent feature vector data structure 928, as represented by ellipsis988.

The feature vector ID 980 can be any type of ID including analphanumeric ID, a numeric ID, a GUID, etc. The feature vector ID 980may include any type of information needed to uniquely identify thatfeature vector 928 compared to other feature vectors 928 associated withthe segment data structure 908. Thus, the feature vector ID 980 needinclude many digits to identify the feature vector 928 compared to theother feature vectors 928 as there be a more constrained number offeature vectors 928 associated with each segment 908.

The latitude 916 and longitude 920 may be the same or similar to thedata provided in latitude 916 and longitude 920 in data structure 908.The latitude 916 and longitude 920 values may be different from thosegiven in data structure 908 (as the feature vector 852 may be in adifferent location), but may be of similar format or content. Similarly,the GIS information 928 may also have similar content to the GISinformation 924 as described in conjunction with data structure 908 inFIG. 9B. The GIS data 924 can be any information that provides for alocation of the feature vector 928 within an environment.

The appearance characteristics 984 can be any of the characteristics asdescribed previously, describing how a feature vector appears in themachine view. For example, feature vectors 852 can include any type ofline lengths, corners, angles, etc. dealing with one or more of thefeature vectors 852. Further, the appearance characteristics 984 canprovide information regarding how that feature vector 852 is associatedwith other feature vectors 852 within the view. For example, the anglesor orientation of feature vector 852C-852H, in view 840 of FIG. 8H, maybe described in appearance characteristics 984. Appearancecharacteristics 984 can also include the actual HD image of the view orthe feature vector 852. As such, the feature vector information 928 canbe used to identify or compare screen views 840 with information withinthe feature vector data structure 928 to determine similarities orlikenesses.

View position 986 can be the position of any feature vector 852 within aview. For example, the view position 986 can be provided with gridcoordinates for feature vectors 852 within view 840. In other words,view 840 may be separated into X and Y coordinates along a vertical andhorizontal axis, respectively, that granulate or represent positionswithin the view 840. The view position 986 may be given by X and Ycoordinates for some part or feature of the feature vector 852 todetermine the feature vector's position within the view. In otherconfigurations, the view position 986 can be a vector or angle anddistance from a certain, predetermined point, such as a bottom left-handcorner of the view. In other configurations, there may be relationshipsor position determinations between the different feature vectors byangles, lines, and/or distances that provide a view position of onefeature vector 852 with respect to another feature vector 852. There maybe other methods for determining the view position of feature vectors928 within views to determine if the current view is similar to one ofthe feature vector data structures 928 and thus to the segmentinformation 908.

A method 1000 for storing information associated with a segment 824 of aroute 816 may be as shown in FIG. 10. A general order for the steps ofthe method 1000 is shown in FIG. 10. Generally, the method 1000 startswith a start operation 1004 and ends with operation 1028. The method1000 can include more or fewer steps or can arrange the order of thesteps differently than those shown in FIG. 10. The method 1000 can beexecuted as a set of computer-executable instructions executed by acomputer system or processor and encoded or stored on a computerreadable medium. In other configurations, the method 1000 may beexecuted by a series of components, circuits, gates, etc. created in ahardware device, such as a System on Chip (SOC), Application SpecificIntegrated Circuit (ASIC), and/or a Field Programmable Gate Array(FPGA). Hereinafter, the method 1000 shall be explained with referenceto the systems, components, circuits, modules, software, datastructures, signaling processes, models, environments, vehicles, etc.described in conjunction with FIGS. 1-9.

A location module 333 of a navigation subsystem 302 may determine thelocal location, in step 1008. A location module 333 may receiveinformation from GPS antenna receiver 331 or sensors 304 to determine aspecific segment 824 or a general location 820 upon which the vehicle100 is currently located. This location may be associated with one ormore segments 824A-824H, depending on the accuracy of the informationreceived by the location module 333. The location module can then use,for example, the process 1100, described in FIG. 11, to determine theexact segment 824 upon which the vehicle is located. The location module333 may then provide information about that feature vector segment 908.Thus, the location module 333 may generate, store, or include any oftype of location information or other information into the datastructure 908. For example, the location module 333 can create thesegment ID 912, the latitude 916, the longitude 920, and/or the GISinformation 924. This segment information 908 describes a specificlocation for the segment 824 and allow the feature vector information928 to be placed into the data structure 908.

The location module 333 may then receive sensor information 304 todetermine the feature vectors in view of the sensors 304, in step 1012.Here, the location module 332 can receive the view 832. The view 832 canbe from a camera 332 or from one or more of the other sensors 304. Thefeature vector view 832 may be only a single machine view image of theview 832 from the sensors 304. As such, only one view 832 may beassociated with the feature vector segment 824 at which the vehicle 100is currently located.

From the view 832, the location module 333 and/or the processor 708 candetermine, using machine vision or some other type of process, thecharacterizations 984, 986 of the feature vectors 928, in step 1016. Theprocessor 706 and/or location module 333 may identify the differentfeature vectors 852 in the view 832. For example, the different circles852, as shown in FIG. 8F, may be created that identify one or moreoutstanding characteristics of the view 836. These outstandingcharacteristics can include such things as building corners, doorways,fire hydrants, landscaping, clocks, other architectural features, or anyother type of characteristic of the surrounding environment that may beeasily identified through machine vision based on the lines orconfiguration of that element. These different feature vectors 852 maythen each have a separate data structure 928 created therefore, andthus, the processor 708 and/or the location module 333 can create thefeature vector ID 980, the latitude 916, the longitude 920, the GISinformation 924, etc.

Further, the location module 333 or processor 708 can also include theappearance characteristics 984 and/or the view position 986. Thecharacteristics 84 may be the types of lines, lengths of lines, anglesbetween lines, or other types of information that the vehicle 100 canuse to characterize the feature vector 928 for future comparison.Further, the view position 986 may be described as where those featurevectors 852 are within the view 836 either by a grid, by an angle, orsome other type of information. Thus, the location module 333 orprocessor 708 can devolve or can change the view 832 into the viewrepresentation 836 shown in 8G, where the feature vectors 852 are shownbut nothing else. Each of these different feature vectors 852 may thenbe characterized by their appearance and may also then be located withinthe view 836. Then the view 836 may be changed to that view 840 shown inFIG. 8H where only the feature vectors 852 are shown and no other typesof information, thus the resultant data structure 928 is compact andeasier to access and to use to compare to views in the future. Further,the appearance characteristics 984 can include the actual HD imagecreated by the above process.

The location module 333 may then store these characteristics 984 intothe maps database 335 and/or the navigation source 356, and step 1020.In some configurations, the location module 333 may send the segmentinformation 908 and/or feature vector information 928 through acommunication interface 350 to the navigation source 356 to providethose segment characterizations 908 of the feature vectors 928associated with the segment 824 to an external source, in step 1024.Thus, each vehicle 100 can provide views of a certain feature vectorsegment 824 that may then be used to build a database for all vehicles100. In this way, the feature vectors 928 may be populated by thevehicles 100 driving the routes 816, where each feature vector 928 isassociated with a segment 908, and may be updated or changed as neededas the environment around the route 816 changes to increase or eliminatecertain feature vectors 852 depending on the changing environment.

An embodiment of a method 1100 for navigating using the route segmentsand for vehicle localization may be as shown in FIG. 11. A general orderfor the steps of the method 1100 is shown in FIG. 11. Generally, themethod 1100 starts with a start operation 1104 and ends with operation1136. The method 1100 can include more or fewer steps or can arrange theorder of the steps differently than those shown in FIG. 11. The method1100 can be executed as a set of computer-executable instructionsexecuted by a computer system or processor and encoded or stored on acomputer readable medium. In other configurations, the method 1100 maybe executed by a series of components, circuits, gates, etc. created ina hardware device, such as a System on Chip (SOC), Application SpecificIntegrated Circuit (ASIC), and/or a Field Programmable Gate Array(FPGA). Hereinafter, the method 1100 shall be explained with referenceto the systems, components, circuits, modules, software, datastructures, signaling processes, models, environments, vehicles, etc.described in conjunction with FIGS. 1-10.

Location module 333 can receive a primary location from a GPS antennareceiver 331 or through some other process, in step 1108. Step 1108 maybe optional as the location module 333 may not be able to receive a GPSposition from GPS antenna receiver 331 due to interference frombuildings, trees, or other structures around the vehicle 100 or may notbe able to identify an initial position through other means. However, ifthe location module 333 can receive a primary location, for example, alocation within area 820, the number of feature vectors 852 or segments824 needed to be viewed by the location module 333 is reduced.

In other circumstances, the primary location may be received ordetermined through dead reckoning or some other type of process, such asvisual odometry and/or particle filtering. Dead reckoning and visualodometry are both processes of estimating a position based on the lastknown position, a route travel based on turns sensed by the locationmodule 333, and distance traveled based on speed of the vehicle 100 overthe time of travel (which can be estimated from a speedometer or throughvisual odometry). From dead reckoning, the location module 333 can alsominimize the number of feature vectors needed to be reviewed.

The location module 333 can then determine the feature vectors 852 thatare in view of the sensors 304, in step 1112. In embodiments, thelocation module 333 can weight lower or filter out segments 824 that arenot within or in physical proximity to area 820. Those lower weighted orfiltered segments 824 are then not considered by the location module333.

For the considered segments 824, the location module 333 can receivefrom a camera sensor 332, infrared sensor 336, or one or more othersensors 304 a view of the surrounding environment similar to those shownin FIGS. 8C-8E. These views may then be analyzed to determine if thereare feature vectors 852 in the views, for example, those feature vectorsrepresented by circles 852 in FIG. 8F. These feature vectors 852 thenmay be extracted or minimized to a view similar to FIG. 8G or 8H. Thisfeature vector information may then be used to compare feature vectorsinformation sensed by the sensors 304 to those feature vectors 928already stored in the navigation source 356 or GIS database 335.

The location module 333 can retrieve the feature vectors information 928already stored in the maps database 335 or navigation source 356, instep 1116. In some configurations, the data in navigation source 356 isupdated to maps database 335 persistently or periodically. Thus, thelocation module 335 need not attempt to extract data from an externalsource but may have all GIS or location information needed in the mapsdatabase 335. Also, the location module 333 can retrieve the one or moredata structures 908 associated with segments 828 and/or 824 within thedetermined primary location 820. As such, the location module 333 maynot attempt to compare the feature vectors view 852, determined in step1112, with every segment 824 along route 816. However, if the filteringof the segments 824 is not completed, it may also be possible, as thevehicle 100 is traveling, that the location module 333 may process asequence of segment views 904 through several iterations to solve for afinal localization. Regardless, the location module 333 can retrieve thesegment information 908 for comparison to the generated feature vectorsview 852, determined in step 1112.

The location module 333 may then match the view feature vectors 852,determined in step 1112, with the retrieved feature vectors information928 associated with the segment data structures 908, retrieved in step1116. Thus, the location module 333 can do a visual comparison offeature vectors 852, 928 both viewed and stored to see if there is amatch between the feature vectors. There may be some error in thecomparison as the location module 333 may not be at exactly the locationof the segment 824 and thus the feature vectors 928 may be in differentlocations or appear slightly dissimilar to those feature vectors 852viewed by the vehicle sensors 304. Regardless, the location module 333can compare several different views, retrieved in step 1116, to thedetermined feature vectors view 852, sensed in step 1112. The closestmatch may then be determined.

In some instances, there may be enough segments 824 to compare that anexact match may not be possible in the allotted time before the vehicle100 moves to a next segment 824. In such situations, the location module333 may determine two or more possible matches, which may put thesegment 824 identified for the vehicle 100 somewhere along route 816 orat two or more specific points. At a next time, when the location module333 attempts to find the next segment 824 where the vehicle 100 shouldhave traveled to, the location module 333 may compare feature vectors852 to a reduced set of segment feature vectors 928 that would beassociated with the previously determined set of possible segments 824.In this way, through two or more iterations of the comparisons, thelocation module 333 can rapidly determine the segment location 824 basedoff a process of elimination of unlikely segments 824. Regardless, oncea match is made by the location module 333, the location module 333 candetermine the location from the match, in step 1124.

The location module 333, once a match is made to the feature vectors928, based on appearance characteristics 984 and view position 986, candetermine the segment 824 associated with those feature vectors 928, indata structure 908. This segment 908 may then read or extract locationinformation (e.g., latitude 916, longitude 920, GIS information 924)from the data structure 908. This location information 916-924 can thenprecisely locate the vehicle 100. As such, the location module 333 thenknows the exact position of the vehicle 100 based on this segmentinformation 908.

In some instances, there still may be an error between the segmentidentified 908 and the actual location of the vehicle 100. For example,if the precise location of the vehicle 100 is not exactly on the segment824, there may be error within the view of the feature vectors whencompared to the feature vectors information 928. However, based on theview error and where that error is occurring or how the error isoccurring, the location module 333 can still determine a precisedistance from the segment location 824 to provide that precise locationfor the vehicle 100. For example, if the feature vectors 852 are shiftedat a certain angle and distance from the view of the feature vectors928, that angle and distance can be correlated into a derived error(both in distance and angle) from the segment 824. For example, if thefeature vectors 852, in FIG. 8F, appear shifted two pixels to the right,the view and the position of the vehicle may be from a location that isan inch to the left of segment location 825.

Based on the determined location, from step 1124, the location module333 can then determine the next segment 824 based on the driving vector,in step 1128. The driving vector may be determined from the location ofprevious segment(s), a route/direction of travel, a speed of travel,and/or based on other sensor information from sensors 304, the vehiclecontrol system 348, and/or the GPS antenna receiver 331. This travelvector information determines the direction of travel and the speed.Based on these two items of information, location module 333 candetermine which of the next segments 824A-824H may be within the vectorof travel 816. Based on this information, the vehicle 100 caniteratively return to step 1112 to compare determined feature vectors852 from the new location 824 to retrieve feature vectors 928 associatedwith that next segment, in step 1128. Thus, the method 1100 can continueiteratively during the entire period of travel for vehicle 100. The moreiterations during the route 816, the more precise the localization is.

Various alternative embodiments and implementations are contemplatedbased on the methods and systems described above. For example, analternative embodiment can use road Segment Definition (SD) mapsrepresented by the segment data stored in the GIS information 904described above. As described, this segment data can include informationlike road width, curvature, number of lanes etc., along the roadnetworks. A traditional Monte-Carlo Localization algorithm can be usedto localize the vehicle 100 along the road networks defined in thesemaps. According to one embodiment, the information defining roadnetworks in the segment definition maps of the GIS information 904 canbe augmented with feature descriptors which are extracted using thecamera images collected during the mapping procedure. For example, themetadata 932 or other elements of the GIS information 904 data structure908 can be extended or modified to include the feature descriptors. AHierarchical Bag of Words (BoW) and Inverted File Indexing method canthen be used for finding the closest matching visual descriptor fromdescriptors that are stored in the GIS information 904 and the positionand orientation information weakly for the matching descriptor can beused to find the global position and orientation of the vehicle moreaccurately and at much faster rate. Examples of such embodiments will bedescribed in greater detail below with reference to FIGS. 12 and 13.

A method 1200 for storing information associated with a segment 824 of aroute 816 may be as shown in FIG. 12. A general order for the steps ofthe method 1200 is shown in FIG. 12. Generally, the method 1200 startswith a start operation 1204 and ends with operation 1228. The method1200 can include more or fewer steps or can arrange the order of thesteps differently than those shown in FIG. 12. The method 1200 can beexecuted as a set of computer-executable instructions executed by acomputer system or processor and encoded or stored on a computerreadable medium. In other configurations, the method 1200 may beexecuted by a series of components, circuits, gates, etc. created in ahardware device, such as a System on Chip (SOC), Application SpecificIntegrated Circuit (ASIC), and/or a Field Programmable Gate Array(FPGA). Hereinafter, the method 1200 shall be explained with referenceto the systems, components, circuits, modules, software, datastructures, signaling processes, models, environments, vehicles, etc.described in conjunction with FIGS. 1-9.

Similar to the process described above with reference to FIG. 10, alocation module 333 of a navigation subsystem 302 may determine thelocal location, in step 1208. A location module 333 may receiveinformation from GPS antenna receiver 331 or sensors 304 to determine aspecific segment 824 or a general location 820 upon which the vehicle100 is currently located. This location may be associated with one ormore segments 824A-824H, depending on the accuracy of the informationreceived by the location module 333. The location module can then use,for example, the process 1300, described in FIG. 13, to determine theexact segment 824 upon which the vehicle is located. The location module333 may then provide information about that feature vector segment 908.Thus, the location module 333 may generate, store, or include any oftype of location information or other information into the datastructure 908. For example, the location module 333 can create thesegment ID 912, the latitude 916, the longitude 920, and/or the GISinformation 924. This segment information 908 describes a specificlocation for the segment 824 and allow the feature vector information928 to be placed into the data structure 908.

The location module 333 may then receive sensor information 304 todetermine the feature vectors in view of the sensors 304, in step 1212.Here, the location module 332 can receive the view 832. The view 832 canbe from a camera 332 or from one or more of the other sensors 304. Thefeature vector view 832 may be only a single machine view image of theview 832 from the sensors 304. As such, only one view 832 may beassociated with the feature vector segment 824 at which the vehicle 100is currently located.

From the view 832, the location module 333 and/or the processor 708 candetermine, using machine vision or some other type of process, thecharacterizations 984, 986 of the feature vectors 928, in step 1216. Theprocessor 706 and/or location module 333 may identify the differentfeature vectors 852 in the view 832. For example, the different circles852, as shown in FIG. 8F, may be created that identify one or moreoutstanding characteristics of the view 836. These outstandingcharacteristics can include such things as building corners, doorways,fire hydrants, landscaping, clocks, other architectural features, or anyother type of characteristic of the surrounding environment that may beeasily identified through machine vision based on the lines orconfiguration of that element. These different feature vectors 852 maythen each have a separate data structure 928 created therefore, andthus, the processor 708 and/or the location module 333 can create thefeature vector ID 980, the latitude 916, the longitude 920, the GISinformation 924, etc.

Further, the location module 333 or processor 708 can also include theappearance characteristics 984 and/or the view position 986. Thecharacteristics 84 may be the types of lines, lengths of lines, anglesbetween lines, or other types of information that the vehicle 100 canuse to characterize the feature vector 928 for future comparison.Further, the view position 986 may be described as where those featurevectors 852 are within the view 836 either by a grid, by an angle, orsome other type of information. Thus, the location module 333 orprocessor 708 can devolve or can change the view 832 into the viewrepresentation 836 shown in 8G, where the feature vectors 852 are shownbut nothing else. Each of these different feature vectors 852 may thenbe characterized by their appearance and may also then be located withinthe view 836. Then the view 836 may be changed to that view 840 shown inFIG. 8H where only the feature vectors 852 are shown and no other typesof information, thus the resultant data structure 928 is compact andeasier to access and to use to compare to views in the future. Further,the appearance characteristics 984 can include the actual HD imagecreated by the above process.

The location module 333 may also extract descriptors from images thatare collected during the mapping procedure at step 1221. For example,Oriented-Rotational Brief (ORB) descriptors may be extracted.ORB-descriptors build on the Features from Accelerated Segment Test(FAST) algorithm keypoint detector and the Binary Robust IndependentElementary Features (BRIEF) descriptor. ORB is basically a fusion of theFAST keypoint detector algorithm and BRIEF descriptors with manymodifications to enhance the performance. It employs the FAST algorithmto find keypoints followed by the application of the Harris cornermeasure to find a set of top N points among them. It can use pyramid toproduce multiscale-features. Because the FAST algorithm fails to computethe orientation, ORB descriptors are based on the intensity weightedcentroid of the patch with a located corner at center. The vectordirection from this corner point to the centroid yields the orientation.To improve the rotation invariance, moments are computed with x and ywhich should be in a circular region of radius r, where r is the size ofthe patch. ORB uses BRIEF descriptors. ORB “steers” BRIEF according tothe orientation of keypoints. For any feature set of n binary tests atlocation (x_(i),y_(i)), defines a 2×n matrix, S which contains thecoordinates of these pixels. Then using the orientation of patch, θ, itsrotation matrix is found and rotates the S to get steered(rotated)version S_(θ). ORB discretizes the angle to increments of 2π/30 (12degrees), and constructs a lookup table of precomputed BRIEF patterns.As long as the keypoint orientation θ is consistent across views, thecorrect set of points S_(θ) will be used to compute its descriptor. InBRIEF, each bit feature has a large variance and a mean near 0.5. Butonce it is oriented along a keypoint direction, it loses this propertyand becomes more distributed. ORB runs a greedy search among allpossible binary tests to find the ones that have both high variance andmeans close to 0.5, as well as being uncorrelated. The result is calledrBRIEF. For descriptor matching, multi-probe Locality Sensitive Hashing(LSH) which improves on the traditional LSH, is used.

Once the GIS information 904 has been moved from the image space to thedescriptor space, a vocabulary of visual words that appears in all theimages that are collected can be created using a Hierarchical Bag ofWords (BoW). BoW refers to an algorithm used for image classification,by treating image features as words. In computer vision, a bag of visualwords is a vector of occurrence counts of a vocabulary of local imagefeatures. To represent an image using the BoW model, an image can betreated as a document. Similarly, “words” in images are defined. Toachieve this, it usually includes following three steps: featuredetection, feature description, and codebook generation. A definition ofthe BoW model can be the “histogram representation based on independentfeatures.” Content based image indexing and retrieval (CBIR) appears tobe the early adopter of this image representation technique. Afterfeature detection, each image is abstracted by several local patches.Feature representation methods deal with how to represent the patches asnumerical vectors. These vectors are called feature descriptors. A gooddescriptor should have the ability to handle intensity, rotation, scaleand affine variations to some extent. One of the most famous descriptorsis Scale-Invariant Feature Transformation (SIFT). SIFT converts eachpatch to a 128-dimensional vector. After this step, each image is acollection of vectors of the same dimension (128 for SIFT), where theorder of different vectors is of no importance. The final step for theBoW model is to convert vector-represented patches to “codewords”(analogous to words in text documents), which also produces a “codebook”(analogy to a word dictionary). A codeword can be considered as arepresentative of several similar patches. One simple method isperforming k-means clustering over all the vectors. Codewords are thendefined as the centers of the learned clusters. The number of theclusters is the codebook size (analogous to the size of the worddictionary). Thus, each patch in an image is mapped to a certaincodeword through the clustering process and the image can be representedby the histogram of the codewords. A number of learning and recognitionmethods have been developed based on the BoW model. Generative models,such as Naïve Bayes classifier and hierarchical Bayesian models, anddiscriminative models, such as pyramid match kernel are examples.

K-means clustering refers to a method of vector quantization, originallyfrom signal processing, that is popular for cluster analysis in datamining. k-means clustering aims to partition n observations into kclusters in which each observation belongs to the cluster with thenearest mean, serving as a prototype of the cluster. This results in apartitioning of the data space into Voronoi cells. The problem can becomputationally difficult (NP-hard). However, there are efficientheuristic algorithms that are commonly employed and converge quickly toa local optimum. These are usually similar to theexpectation-maximization algorithm for mixtures of Gaussiandistributions via an iterative refinement approach employed by bothalgorithms. Additionally, they both use cluster centers to model thedata. However, k-means clustering tends to find clusters of comparablespatial extent, while the expectation-maximization mechanism allowsclusters to have different shapes. The algorithm has a looserelationship to the k-nearest neighbor classifier, a popularmachine-learning technique for classification that is often confusedwith k-means because of the k in the name. One can apply the l-nearestneighbor classifier on the cluster centers obtained by k-means toclassify new data into the existing clusters. This is known as nearestcentroid classifier or the Rocchio algorithm. The most common algorithmuses an iterative refinement technique, which is often called thek-means algorithm. It is also referred to as Lloyd's algorithm,particularly in the computer science community.

The main purpose of creating a set of vocabulary from images, is that ithelps to represent any image using s set of words, where s e k reducesthe complexity and memory footprints. After creating the vocabulary, adatabase can be created, e.g., GIS information 904, having visual wordsextracted from images collected during the mapping procedure and theircorresponding position and orientation which are obtained using RTK-GPS,i.e., the road segment definition map of the GIS information 904described above is augmented with the generated vocabulary, e.g., storedin the metadata 932 or elsewhere. To localize the vehicle, the InvertedFile Indexing method can be utilized for fast retrieval of posecorresponding to the image that the vehicle sees.

The Inverted File Indexing method refers to an index data structurestoring a mapping from content, such as words or numbers, to itslocations in a database file, or in a document or a set of documents(named in contrast to a forward index, which maps from documents tocontent). The purpose of an inverted index is to allow fast objectsearches at a cost of increased processing when an object is added tothe database. The inverted file may be the database file itself, ratherthan its index. There are two main variants of inverted indexes. Arecord-level inverted index or inverted file index or just inverted filecontains a list of references to objects for each word. A word-levelinverted index or full inverted index or inverted list additionallycontains the positions of each word within an object. This methodaccelerates the retrieval of the closest matching position andorientation of the vehicle. If not, the current view is matched witheach information in the database. The inverted file indexing method canbe thought of as an index page in the back of a book. Instead of a wordpointing to a page number, the index points to a set of visual words tocorresponding images.

In some configurations, the location module 333 may send the segmentinformation 908, feature vector information 928 and/or descriptorvocabulary through a communication interface 350 to the navigationsource 356 to provide those segment characterizations 908 of the featurevectors 928 associated with the segment 824 to an external source, instep 1224. Thus, each vehicle 100 can provide views of a certain featurevector segment 824 that may then be used to build a database for allvehicles 100. In this way, the feature vectors 928 and/or descriptorvocabulary may be populated by the vehicles 100 driving the routes 816,where each feature vector 928 and/or descriptor vocabulary areassociated with a segment 908, and may be updated or changed as neededas the environment around the route 816 changes to increase or eliminatecertain feature vectors 852 depending on the changing environment.

An embodiment of a method 1300 for navigating using the route segmentsand for vehicle localization may be as shown in FIG. 13. A general orderfor the steps of the method 1300 is shown in FIG. 13. Generally, themethod 1300 starts with a start operation 1304 and ends with operation1336. The method 1300 can include more or fewer steps or can arrange theorder of the steps differently than those shown in FIG. 13. The method1300 can be executed as a set of computer-executable instructionsexecuted by a computer system or processor and encoded or stored on acomputer readable medium. In other configurations, the method 1300 maybe executed by a series of components, circuits, gates, etc. created ina hardware device, such as a System on Chip (SOC), Application SpecificIntegrated Circuit (ASIC), and/or a Field Programmable Gate Array(FPGA). Hereinafter, the method 1300 shall be explained with referenceto the systems, components, circuits, modules, software, datastructures, signaling processes, models, environments, vehicles, etc.described in conjunction with FIGS. 1-12.

Similar to the process described above with reference to FIG. 11,location module 333 can receive a primary location from a GPS antennareceiver 331 or through some other process, in step 1308. Step 1308 maybe optional as the location module 333 may not be able to receive a GPSposition from GPS antenna receiver 331 due to interference frombuildings, trees, or other structures around the vehicle 100 or may notbe able to identify an initial position through other means. However, ifthe location module 333 can receive a primary location, for example, alocation within area 820, the number of feature vectors 852 or segments824 needed to be viewed by the location module 333 is reduced.

In other circumstances, the primary location may be received ordetermined through dead reckoning or some other type of process, such asvisual odometry and/or particle filtering. Dead reckoning and visualodometry are both processes of estimating a position based on the lastknown position, a route travel based on turns sensed by the locationmodule 333, and distance traveled based on speed of the vehicle 100 overthe time of travel (which can be estimated from a speedometer or throughvisual odometry). From dead reckoning, the location module 333 can alsominimize the number of feature vectors needed to be reviewed.

The location module 333 can then determine the feature vectors 852 thatare in view of the sensors 304, in step 1312. In embodiments, thelocation module 333 can weight lower or filter out segments 824 that arenot within or in physical proximity to area 820. Those lower weighted orfiltered segments 824 are then not considered by the location module333.

For the considered segments 824, the location module 333 can receivefrom a camera sensor 332, infrared sensor 336, or one or more othersensors 304 a view of the surrounding environment similar to those shownin FIGS. 8C-8E. These views may then be analyzed to determine if thereare feature vectors 852 in the views, for example, those feature vectorsrepresented by circles 852 in FIG. 8F. These feature vectors 852 thenmay be extracted or minimized to a view similar to FIG. 8G or 8H. Thisfeature vector information may then be used to compare feature vectorsinformation sensed by the sensors 304 to those feature vectors 928already stored in the navigation source 356 or GIS database 335.

The location module 333 can retrieve the feature vectors information 928already stored in the maps database 335 or navigation source 356, instep 1316. The map itself is a set of linearly spaced points. Accordingto one embodiment, the slope between each set of points can becalculated and interpolated between them to create closely spaced pointssuch that a segment map point can be found in every half-a-meter. Thisis done for finding distance between a particle and its closest mappoint in an efficient way using KD-trees as will be described in greaterdetail below.

To solve the problem of localization, particle filters can be applied atstep 1320. The output of the particle filter algorithm can be aprobability distribution over the pose x_(t) of the vehicle. Typically,the particle filter algorithm has a motion update step, weight updatestep and resampling step. The algorithm has a set of particles P. Eachparticle can have the states {x_(i), y_(i) θ₁, w_(i), d_(i)} where x₁,y_(i) represents the position of the i^(th) particle θ_(i) orientationof the i^(th) particle. w_(i) and d_(i) represents the weight assignedand distance from the closest map point of the i^(th) particle.

Each of the particle by themselves represent a possible location for thevehicle. When the vehicle moves from one position to another position inthe real world, each of the particles should move from its currentposition and orientation similar to the actual movement of the vehicle.The motion update step can be generally represented by p(X_(t)/U_(t),X_(t-1), M), Here X_(t) and X_(t-1) represent the pose of the vehicle attime t and t−1, U_(t) represents the control input given to the vehicleand M represents the road segment definition map. Let x_(t), y_(t),θ_(t) be the pose of the vehicle at time t and x_(t-1), y_(t-1), θ_(t-1)be pose of the pose of the vehicle at time t−1. The following equationsare used to update the pose of the particle as the vehicle moves in theworld:

δ_(trans)=√{square root over ((x _(t) −x _(t-1))+(y _(t) −y _(t-1)))}

δ_(rot1)=α tan 2((y _(t) −y _(t-1)),(x _(t) −x _(t-1)))

δ_(rot2) =θt−θ _(t-)−δ_(rot1)

x _(t) =x _(t-1)+cos(θ_(t-1)+δ_(rot1))+

(0,σ_(x) ²)

y _(t) =y _(t-1)+sin(θ_(t-1)++δ_(rot1))+

(0,σ_(y) ²)

θ_(t)=θ_(t-1)+δ_(rot1)+

(0,σ_(θ) ²)

Here

(0,σ_(x) ²),

(0,σ_(y) ²),

(0,σ_(θ) ²) represents the gaussian noise added to each state of theparticle with mean zero and standard deviation of σ_(x), σ_(y), σ_(θ)correspondingly. To get the position and orientation of the vehicle atthe next time step, a visual odometry system fused with IMU can be used.This fusion gives the relative transformation from time t−1 to t. Sincethe particle filter framework has a probabilistic transition model, thedrift accumulated due to incremental pose estimation can be eliminated.

As the pose of the particles are propagated similar to the vehiclemotion, a weight can be assigned to each of the particles. Each particlecan be assigned a weight w_(i)=p(Z_(t)/X_(t),M) where w_(i) is theweight of i^(th) particle and Z_(t) represents the observations receivedat time t. The distance of the particle from the closest segment pointcan be used for weighting the particles. Assuming that the vehicle movesonly along the road network segments, the closest map point to thecurrent particle can be found and a weight can be assigned to it basedon the following equation:

$w_{i} = {\frac{1}{\sqrt{2{\pi\sigma}_{d}^{2}}}e^{{- d_{i}^{2}}\text{/}2\sigma_{d}^{2}}}$

Here, σ_(d) is the standard deviation distance metric and d_(i) isdistance of the particle from the closest map point. This intuitivelymeans that, as a particle moves away from the segment map, the distanceof the particle to the closest map point is going to increase because ofwhich it will be assigned a lower weight. Only the particles whichtravel along the segments will be assigned with higher weights.

Since the number of map points between two segment points have beenaugmented, the closest map point to the current particle pose can befound using a KD-tree. KD-Trees refers to a space-partitioning datastructure for organizing points in a k-dimensional space. KD trees are auseful data structure for several applications, such as searchesinvolving a multidimensional search key (e.g., range searches andnearest neighbor searches). KD trees are a special case of binary spacepartitioning trees. The nearest neighbor search (NN) algorithm aims tofind the point in the tree that is nearest to a given input point. Thissearch can be done efficiently by using the tree properties to quicklyeliminate large portions of the search space. Searching for a nearestneighbor in a KD tree includes the steps: starting with the root node,the algorithm moves down the tree recursively (i.e., it goes left orright depending on whether the point is lesser than or greater than thecurrent node in the split dimension); when the algorithm reaches a leafnode, it saves that node point as the “current best”; the algorithmunwinds the recursion of the tree, performing the following steps ateach node: when the current node is closer than the current best, thenit becomes the current best, the algorithm checks whether there could beany points on the other side of the splitting plane that are closer tothe search point than the current best. In concept, this is done byintersecting the splitting hyperplane with a hypersphere around thesearch point that has a radius equal to the current nearest distance.Since the hyperplanes are all axis-aligned this is implemented as asimple comparison to see whether the distance between the splittingcoordinate of the search point and current node is lesser than thedistance (overall coordinates) from the search point to the currentbest, when the hypersphere crosses the plane, there could be nearerpoints on the other side of the plane, so the algorithm must move downthe other branch of the tree from the current node looking for closerpoints, following the same recursive process as the entire search, andwhen the hypersphere doesn't intersect the splitting plane, then thealgorithm continues walking up the tree, and the entire branch on theother side of that node is eliminated; and when the algorithm finishesthis process for the root node, then the search is complete. Generally,the algorithm uses squared distances for comparison to avoid computingsquare roots. Additionally, it can save computation by holding thesquared current best distance in a variable for comparison. The KD-treeis useful to retrieve the closest map point to the current particleposition in O(log n) where n is the number of map points in the tree.The shortest distance between a point and line equation can also be usedto find the distance between a particle and the closest segment, butthis distance should be calculated with respect to each of the segmentsin the map and this would be very inefficient.

With the closest matching position and orientation in hand, theparticles which are within a certain region from that position can befound. These particles can be weighted according to the following:

$w_{i} = \{ \begin{matrix}{{w_{i} + {\frac{1}{\sqrt{2{\pi\sigma}_{i}^{2}}}e^{{- l_{i}^{2}}\text{/}2\sigma_{i}^{2}}}},{{{if}\mspace{14mu} l_{i}} < {5.0\mspace{14mu} m}}} \\{0,{otherwise}}\end{matrix} $

Here, I_(i) indicates the distance between the i^(th) particle and theretrieved image pose from the database, σ_(i) is standard deviation. Inthis way, the weights for all the particles that are close to thematched position and orientation are increased. According to oneembodiment, particle filtering can further comprise a resampling step.By resampling, bad particles can be eliminated and only the goodparticles can propagate through the environment. A stochastic universalresampling method can be used to resample all particles and it has acomplexity of 0(log n). Thus, a true location and pose of the vehiclecan be determined at step 1324 in a very short period of time.

Based on the determined location, from step 1324, the location module333 can then determine the next segment 824 based on the driving vector,in step 1328. The driving vector may be determined from the location ofprevious segment(s), a route/direction of travel, a speed of travel,and/or based on other sensor information from sensors 304, the vehiclecontrol system 348, and/or the GPS antenna receiver 331. This travelvector information determines the direction of travel and the speed.Based on these two items of information, location module 333 candetermine which of the next segments 824A-824H may be within the vectorof travel 816. Based on this information, the vehicle 100 caniteratively return to step 1312 to compare determined feature vectors852 from the new location 824 to retrieve feature vectors 928 associatedwith that next segment, in step 1328. Thus, the method 1300 can continueiteratively during the entire period of travel for vehicle 100. The moreiterations during the route 816, the more precise the localization is.

Through this process 1300, the location module 333 can get a very exactlocation, possibly down to inches or millimeters. This exact locationallows for automated driving that is much safer and is in less need ofconstant sensor input to determine the location of the vehicle 100.Rather than just looking for objects to avoid around the vehicle 100 orsimply trying to maintain a certain spacing between sensed objects, thevehicle 100 can know an exact location, be able to maintain thevehicle's position on a roadway using this localization, and thenenhance the performance of the vehicle in an autonomous mode bycomplementing the sensed position with the exact localization. Further,turns and other types of maneuvers may be more exact as the localizationcan also complement the driving system's use of sensors in theenvironment to derive an exact position and then pair this exactposition with the current sensed position of the car in the environment.Thus, the vehicle's autonomous driving capabilities are greatly enhancedwith the exact localization described herein.

The exemplary systems and methods of this disclosure have been describedin relation to vehicle systems and electric vehicles. However, to avoidunnecessarily obscuring the present disclosure, the precedingdescription omits a number of known structures and devices. Thisomission is not to be construed as a limitation of the scope of theclaimed disclosure. Specific details are set forth to provide anunderstanding of the present disclosure. It should, however, beappreciated that the present disclosure may be practiced in a variety ofways beyond the specific detail set forth herein.

Furthermore, while the exemplary embodiments illustrated herein show thevarious components of the system collocated, certain components of thesystem can be located remotely, at distant portions of a distributednetwork, such as a LAN and/or the Internet, or within a dedicatedsystem. Thus, it should be appreciated, that the components of thesystem can be combined into one or more devices, such as a server,communication device, or collocated on a feature vector node of adistributed network, such as an analog and/or digital telecommunicationsnetwork, a packet-switched network, or a circuit-switched network. Itwill be appreciated from the preceding description, and for reasons ofcomputational efficiency, that the components of the system can bearranged at any location within a distributed network of componentswithout affecting the operation of the system.

Furthermore, it should be appreciated that the various links connectingthe elements can be wired or wireless links, or any combination thereof,or any other known or later developed element(s) that is capable ofsupplying and/or communicating data to and from the connected elements.These wired or wireless links can also be secure links and may becapable of communicating encrypted information. Transmission media usedas links, for example, can be any suitable carrier for electricalsignals, including coaxial cables, copper wire, and fiber optics, andmay take the form of acoustic or light waves, such as those generatedduring radio-wave and infra-red data communications.

While the flowcharts have been discussed and illustrated in relation toa feature vector sequence of events, it should be appreciated thatchanges, additions, and omissions to this sequence can occur withoutmaterially affecting the operation of the disclosed embodiments,configuration, and aspects.

A number of variations and modifications of the disclosure can be used.It would be possible to provide for some features of the disclosurewithout providing others.

In yet another embodiment, the systems and methods of this disclosurecan be implemented in conjunction with a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral integratedcircuit element(s), an ASIC or other integrated circuit, a digitalsignal processor, a hard-wired electronic or logic circuit such asdiscrete element circuit, a programmable logic device or gate array suchas PLD, PLA, FPGA, PAL, special purpose computer, any comparable means,or the like. In general, any device(s) or means capable of implementingthe methodology illustrated herein can be used to implement the variousaspects of this disclosure. Exemplary hardware that can be used for thepresent disclosure includes computers, handheld devices, telephones(e.g., cellular, Internet enabled, digital, analog, hybrids, andothers), and other hardware known in the art. Some of these devicesinclude processors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein.

In yet another embodiment, the disclosed methods may be readilyimplemented in conjunction with software using object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer or workstation platforms.Alternatively, the disclosed system may be implemented partially orfully in hardware using standard logic circuits or VLSI design. Whethersoftware or hardware is used to implement the systems in accordance withthis disclosure is dependent on the speed and/or efficiency requirementsof the system, the feature vector function, and the feature vectorsoftware or hardware systems or microprocessor or microcomputer systemsbeing utilized.

In yet another embodiment, the disclosed methods may be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this disclosurecan be implemented as a program embedded on a personal computer such asan applet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Although the present disclosure describes components and functionsimplemented in the embodiments with reference to feature vectorstandards and protocols, the disclosure is not limited to such standardsand protocols. Other similar standards and protocols not mentionedherein are in existence and are considered to be included in the presentdisclosure. Moreover, the standards and protocols mentioned herein andother similar standards and protocols not mentioned herein areperiodically superseded by faster or more effective equivalents havingessentially the same functions. Such replacement standards and protocolshaving the same functions are considered equivalents included in thepresent disclosure.

The present disclosure, in various embodiments, configurations, andaspects, includes components, methods, processes, systems and/orapparatus substantially as depicted and described herein, includingvarious embodiments, sub-combinations, and subsets thereof. Those ofskill in the art will understand how to make and use the systems andmethods disclosed herein after understanding the present disclosure. Thepresent disclosure, in various embodiments, configurations, and aspects,includes providing devices and processes in the absence of items notdepicted and/or described herein or in various embodiments,configurations, or aspects hereof, including in the absence of suchitems as may have been used in previous devices or processes, e.g., forimproving performance, achieving ease, and/or reducing cost ofimplementation.

The foregoing discussion of the disclosure has been presented forpurposes of illustration and description. The foregoing is not intendedto limit the disclosure to the form or forms disclosed herein. In theforegoing Detailed Description for example, various features of thedisclosure are grouped together in one or more embodiments,configurations, or aspects for the purpose of streamlining thedisclosure. The features of the embodiments, configurations, or aspectsof the disclosure may be combined in alternate embodiments,configurations, or aspects other than those discussed above. This methodof disclosure is not to be interpreted as reflecting an intention thatthe claimed disclosure requires more features than are expressly recitedin each claim. Rather, as the following claims reflect, inventiveaspects lie in less than all features of a single foregoing disclosedembodiment, configuration, or aspect. Thus, the following claims arehereby incorporated into this Detailed Description, with each claimstanding on its own as a separate preferred embodiment of thedisclosure.

Moreover, though the description of the disclosure has includeddescription of one or more embodiments, configurations, or aspects andcertain variations and modifications, other variations, combinations,and modifications are within the scope of the disclosure, e.g., as maybe within the skill and knowledge of those in the art, afterunderstanding the present disclosure. It is intended to obtain rights,which include alternative embodiments, configurations, or aspects to theextent permitted, including alternate, interchangeable and/or equivalentstructures, functions, ranges, or steps to those claimed, whether or notsuch alternate, interchangeable and/or equivalent structures, functions,ranges, or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

Embodiments include an autonomous vehicle control system, comprising: asensor, the sensor sensing an environment surrounding a vehicleassociated with the vehicle control system, wherein a feature vectordescribes at least a portion of the environment at a current location ofthe vehicle on a route; a processor communicatively coupled with thesensor, a memory communicatively coupled with and readable by theprocessor and storing therein a set of instructions which, when executedby the processor, causes the processor to determine the current locationof the vehicle on the route by: receiving information from the sensorregarding the feature vector; retrieving feature vector particleinformation associated with two or more segments of a route of travelfor the vehicle, wherein the feature vector particle informationcomprises a vocabulary of visual descriptors; applying particlefiltering to the received information from the sensor regarding thefeature vector and the retrieved feature vector particle informationassociated with two or more segments of a route of travel for thevehicle; and determining the current location of the vehicle on theroute based on the applied particle filtering.

Aspects of the above autonomous vehicle control system include whereinthe vocabulary of visual descriptors is based on Oriented-RotationalBrief (ORB) descriptors for the feature vector particle informationassociated with the two or more segments.

Aspects of the above autonomous vehicle control system include whereinthe vocabulary of visual descriptors is derived from the ORB descriptorsusing a Hierarchical Bag of Words (BoW).

Aspects of the above autonomous vehicle control system include whereinthe feature vector particle information is retrieved using an InvertedFile Index method.

Aspects of the above autonomous vehicle control system include whereinapplying particle filtering to the received information from the sensorregarding the feature vector and the retrieved feature vector particleinformation associated with two or more segments of a route of travelfor the vehicle comprises applying a Monte Carlo localization algorithmto the retrieved feature vector particle information associated with twoor more segments of a route of travel for the vehicle to select a set oflocal particles, updating the selected set of local particles based onmotion of the vehicle, and weighting the updated set of local particlesbased on a distance of each particle in the updated set of localparticles to a closest segment point and further based on an imagedescriptor in the visual vocabulary or each particle.

Aspects of the above autonomous vehicle control system include whereinapplying particle filtering to the received information from the sensorregarding the feature vector and the retrieved feature vector particleinformation associated with two or more segments of a route of travelfor the vehicle further comprises determining the distance of eachparticle in the updated set of local particles to the closest segmentpoint using a KD tree.

Aspects of the above autonomous vehicle control system include whereinapplying particle filtering to the received information from the sensorregarding the feature vector and the retrieved feature vector particleinformation associated with two or more segments of a route of travelfor the vehicle further comprises applying stochastic universalresampling to the updated set of local particles.

Embodiments include a method for determining a current location of avehicle on a route, the method comprising: receiving, by a controlsystem of the vehicle, information from the sensor regarding the featurevector; retrieving, by the control system of the vehicle, feature vectorparticle information associated with two or more segments of a route oftravel for the vehicle, wherein the feature vector particle informationcomprises a vocabulary of visual descriptors; applying, by the controlsystem of the vehicle, particle filtering to the received informationfrom the sensor regarding the feature vector and the retrieved featurevector particle information associated with two or more segments of aroute of travel for the vehicle; and determining, by the control systemof the vehicle, the current location of the vehicle on the route basedon the applied particle filtering.

Aspects of the above method include wherein the vocabulary of visualdescriptors is based on Oriented-Rotational Brief (ORB) descriptors forthe feature vector particle information associated with the two or moresegments.

Aspects of the above method include wherein the vocabulary of visualdescriptors is derived from the ORB descriptors using a Hierarchical Bagof Words (BoW).

Aspects of the above method include wherein the feature vector particleinformation is retrieved using an Inverted File Index method.

Aspects of the above method include wherein applying particle filteringto the received information from the sensor regarding the feature vectorand the retrieved feature vector particle information associated withtwo or more segments of a route of travel for the vehicle comprisesapplying a Monte Carlo localization algorithm to the retrieved featurevector particle information associated with two or more segments of aroute of travel for the vehicle to select a set of local particles,updating the selected set of local particles based on motion of thevehicle, and weighting the updated set of local particles based on adistance of each particle in the updated set of local particles to aclosest segment point and further based on an image descriptor in thevisual vocabulary or each particle.

Aspects of the above method include wherein applying particle filteringto the received information from the sensor regarding the feature vectorand the retrieved feature vector particle information associated withtwo or more segments of a route of travel for the vehicle furthercomprises determining the distance of each particle in the updated setof local particles to the closest segment point using a KD tree.

Aspects of the above method include wherein applying particle filteringto the received information from the sensor regarding the feature vectorand the retrieved feature vector particle information associated withtwo or more segments of a route of travel for the vehicle furthercomprises applying stochastic universal resampling to the updated set oflocal particles.

Embodiments include a non-transitory, computer-readable mediumcomprising a set of instructions stored therein which, when executed bya processor, causes the processor to determine a current location of avehicle on a route by: receiving information from the sensor regardingthe feature vector; retrieving feature vector particle informationassociated with two or more segments of a route of travel for thevehicle, wherein the feature vector particle information comprises avocabulary of visual descriptors; applying particle filtering to thereceived information from the sensor regarding the feature vector andthe retrieved feature vector particle information associated with two ormore segments of a route of travel for the vehicle; and determining thecurrent location of the vehicle on the route based on the appliedparticle filtering.

Aspects of the above non-transitory, computer-readable medium includewherein the vocabulary of visual descriptors is based onOriented-Rotational Brief (ORB) descriptors for the feature vectorparticle information associated with the two or more segments andwherein the vocabulary of visual descriptors is derived from the ORBdescriptors using a Hierarchical Bag of Words (BoW).

Aspects of the above non-transitory, computer-readable medium includewherein the feature vector particle information is retrieved using anInverted File Index method.

Aspects of the above non-transitory, computer-readable medium includewherein applying particle filtering to the received information from thesensor regarding the feature vector and the retrieved feature vectorparticle information associated with two or more segments of a route oftravel for the vehicle comprises applying a Monte Carlo localizationalgorithm to the retrieved feature vector particle informationassociated with two or more segments of a route of travel for thevehicle to select a set of local particles, updating the selected set oflocal particles based on motion of the vehicle, and weighting theupdated set of local particles based on a distance of each particle inthe updated set of local particles to a closest segment point andfurther based on an image descriptor in the visual vocabulary or eachparticle.

Aspects of the above non-transitory, computer-readable medium includewherein applying particle filtering to the received information from thesensor regarding the feature vector and the retrieved feature vectorparticle information associated with two or more segments of a route oftravel for the vehicle further comprises determining the distance ofeach particle in the updated set of local particles to the closestsegment point using a KD tree.

Aspects of the above non-transitory, computer-readable medium includewherein applying particle filtering to the received information from thesensor regarding the feature vector and the retrieved feature vectorparticle information associated with two or more segments of a route oftravel for the vehicle further comprises applying stochastic universalresampling to the updated set of local particles.

Any one or more of the aspects/embodiments as substantially disclosedherein.

Any one or more of the aspects/embodiments as substantially disclosedherein optionally in combination with any one or more otheraspects/embodiments as substantially disclosed herein.

One or more means adapted to perform any one or more of the aboveaspects/embodiments as substantially disclosed herein.

The phrases “at least one,” “one or more,” “or,” and “and/or” areopen-ended expressions that are both conjunctive and disjunctive inoperation. For example, each of the expressions “at least one of A, Band C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “oneor more of A, B, or C,” “A, B, and/or C,” and “A, B, or C” means Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more,” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising,” “including,” and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers toany process or operation, which is typically continuous orsemi-continuous, done without material human input when the process oroperation is performed. However, a process or operation can beautomatic, even though performance of the process or operation usesmaterial or immaterial human input, if the input is received beforeperformance of the process or operation. Human input is deemed to bematerial if such input influences how the process or operation will beperformed. Human input that consents to the performance of the processor operation is not deemed to be “material.”

Aspects of the present disclosure may take the form of an embodimentthat is entirely hardware, an embodiment that is entirely software(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Any combination of one or more computer-readable medium(s) may beutilized. The computer-readable medium may be a computer-readable signalmedium or a computer-readable storage medium.

A computer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a Random-Access Memory (RAM), a Read-Only Memory(ROM), an Erasable Programmable Read-Only Memory (EPROM or Flashmemory), an optical fiber, a portable Compact Disc Read-Only Memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer-readable storage medium may be any tangible medium that cancontain or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer-readable medium may be transmitted using anyappropriate medium, including, but not limited to, wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

The terms “determine,” “calculate,” “compute,” and variations thereof,as used herein, are used interchangeably and include any type ofmethodology, process, mathematical operation or technique.

The term “Electric Vehicle” (EV), also referred to herein as an electricdrive vehicle, may use one or more electric motors or traction motorsfor propulsion. An electric vehicle may be powered through a collectorsystem by electricity from off-vehicle sources, or may be self-containedwith a battery or generator to convert fuel to electricity. An electricvehicle generally includes a Rechargeable Electricity Storage System(RESS) (also called Full Electric Vehicles (FEV)). Power storage methodsmay include: chemical energy stored on the vehicle in on-board batteries(e.g., Battery Electric Vehicle or BEV), on board kinetic energy storage(e.g., flywheels), and/or static energy (e.g., by on-board double-layercapacitors). Batteries, electric double-layer capacitors, and flywheelenergy storage may be forms of rechargeable on-board electrical storage.

The term “hybrid electric vehicle” refers to a vehicle that may combinea conventional (usually fossil fuel-powered) powertrain with some formof electric propulsion. Most hybrid electric vehicles combine aconventional Internal Combustion Engine (ICE) propulsion system with anelectric propulsion system (hybrid vehicle drivetrain). In parallelhybrids, the ICE and the electric motor are both connected to themechanical transmission and can simultaneously transmit power to drivethe wheels, usually through a conventional transmission. In serieshybrids, only the electric motor drives the drivetrain, and a smallerICE works as a generator to power the electric motor or to recharge thebatteries. Power-split hybrids combine series and parallelcharacteristics. A full hybrid, sometimes also called a strong hybrid,is a vehicle that can run on just the engine, just the batteries, or acombination of both. A mid hybrid is a vehicle that cannot be drivensolely on its electric motor, because the electric motor does not haveenough power to propel the vehicle on its own.

The term “Rechargeable Electric Vehicle” or “REV” refers to a vehiclewith on board rechargeable energy storage, including electric vehiclesand hybrid electric vehicles.

What is claimed is:
 1. An autonomous vehicle control system, comprising:a sensor, the sensor sensing an environment surrounding a vehicleassociated with the vehicle control system, wherein a feature vectordescribes at least a portion of the environment at a current location ofthe vehicle on a route; a processor communicatively coupled with thesensor, a memory communicatively coupled with and readable by theprocessor and storing therein a set of instructions which, when executedby the processor, causes the processor to determine the current locationof the vehicle on the route by: receiving information from the sensorregarding the feature vector; retrieving feature vector particleinformation associated with two or more segments of a route of travelfor the vehicle, wherein the feature vector particle informationcomprises a vocabulary of visual descriptors; applying particlefiltering to the received information from the sensor regarding thefeature vector and the retrieved feature vector particle informationassociated with two or more segments of a route of travel for thevehicle; and determining the current location of the vehicle on theroute based on the applied particle filtering.
 2. The control system ofclaim 1, wherein the vocabulary of visual descriptors is based onOriented-Rotational Brief (ORB) descriptors for the feature vectorparticle information associated with the two or more segments.
 3. Thecontrol system of claim 2, wherein the vocabulary of visual descriptorsis derived from the ORB descriptors using a Hierarchical Bag of Words(BoW).
 4. The control system of claim 1, wherein the feature vectorparticle information is retrieved using an Inverted File Index method.5. The control system of claim 1, wherein applying particle filtering tothe received information from the sensor regarding the feature vectorand the retrieved feature vector particle information associated withtwo or more segments of a route of travel for the vehicle comprisesapplying a Monte Carlo localization algorithm to the retrieved featurevector particle information associated with two or more segments of aroute of travel for the vehicle to select a set of local particles,updating the selected set of local particles based on motion of thevehicle, and weighting the updated set of local particles based on adistance of each particle in the updated set of local particles to aclosest segment point and further based on an image descriptor in thevisual vocabulary or each particle.
 6. The control system of claim 5,wherein applying particle filtering to the received information from thesensor regarding the feature vector and the retrieved feature vectorparticle information associated with two or more segments of a route oftravel for the vehicle further comprises determining the distance ofeach particle in the updated set of local particles to the closestsegment point using a KD tree.
 7. The control system of claim 6, whereinapplying particle filtering to the received information from the sensorregarding the feature vector and the retrieved feature vector particleinformation associated with two or more segments of a route of travelfor the vehicle further comprises applying stochastic universalresampling to the updated set of local particles.
 8. A method fordetermining a current location of a vehicle on a route, the methodcomprising: receiving, by a control system of the vehicle, informationfrom the sensor regarding the feature vector; retrieving, by the controlsystem of the vehicle, feature vector particle information associatedwith two or more segments of a route of travel for the vehicle, whereinthe feature vector particle information comprises a vocabulary of visualdescriptors; applying, by the control system of the vehicle, particlefiltering to the received information from the sensor regarding thefeature vector and the retrieved feature vector particle informationassociated with two or more segments of a route of travel for thevehicle; and determining, by the control system of the vehicle, thecurrent location of the vehicle on the route based on the appliedparticle filtering.
 9. The method of claim 8, wherein the vocabulary ofvisual descriptors is based on Oriented-Rotational Brief (ORB)descriptors for the feature vector particle information associated withthe two or more segments.
 10. The method of claim 9, wherein thevocabulary of visual descriptors is derived from the ORB descriptorsusing a Hierarchical Bag of Words (BoW).
 11. The method of claim 8,wherein the feature vector particle information is retrieved using anInverted File Index method.
 12. The method of claim 8, wherein applyingparticle filtering to the received information from the sensor regardingthe feature vector and the retrieved feature vector particle informationassociated with two or more segments of a route of travel for thevehicle comprises applying a Monte Carlo localization algorithm to theretrieved feature vector particle information associated with two ormore segments of a route of travel for the vehicle to select a set oflocal particles, updating the selected set of local particles based onmotion of the vehicle, and weighting the updated set of local particlesbased on a distance of each particle in the updated set of localparticles to a closest segment point and further based on an imagedescriptor in the visual vocabulary or each particle.
 13. The method ofclaim 12, wherein applying particle filtering to the receivedinformation from the sensor regarding the feature vector and theretrieved feature vector particle information associated with two ormore segments of a route of travel for the vehicle further comprisesdetermining the distance of each particle in the updated set of localparticles to the closest segment point using a KD tree.
 14. The methodof claim 13, wherein applying particle filtering to the receivedinformation from the sensor regarding the feature vector and theretrieved feature vector particle information associated with two ormore segments of a route of travel for the vehicle further comprisesapplying stochastic universal resampling to the updated set of localparticles.
 15. A non-transitory, computer-readable medium comprising aset of instructions stored therein which, when executed by a processor,causes the processor to determine a current location of a vehicle on aroute by: receiving information from the sensor regarding the featurevector; retrieving feature vector particle information associated withtwo or more segments of a route of travel for the vehicle, wherein thefeature vector particle information comprises a vocabulary of visualdescriptors; applying particle filtering to the received informationfrom the sensor regarding the feature vector and the retrieved featurevector particle information associated with two or more segments of aroute of travel for the vehicle; and determining the current location ofthe vehicle on the route based on the applied particle filtering. 16.The non-transitory, computer-readable medium of claim 15, wherein thevocabulary of visual descriptors is based on Oriented-Rotational Brief(ORB) descriptors for the feature vector particle information associatedwith the two or more segments and wherein the vocabulary of visualdescriptors is derived from the ORB descriptors using a Hierarchical Bagof Words (BoW).
 17. The non-transitory, computer-readable medium ofclaim 15, wherein the feature vector particle information is retrievedusing an Inverted File Index method.
 18. The non-transitory,computer-readable medium of claim 15, wherein applying particlefiltering to the received information from the sensor regarding thefeature vector and the retrieved feature vector particle informationassociated with two or more segments of a route of travel for thevehicle comprises applying a Monte Carlo localization algorithm to theretrieved feature vector particle information associated with two ormore segments of a route of travel for the vehicle to select a set oflocal particles, updating the selected set of local particles based onmotion of the vehicle, and weighting the updated set of local particlesbased on a distance of each particle in the updated set of localparticles to a closest segment point and further based on an imagedescriptor in the visual vocabulary or each particle.
 19. Thenon-transitory, computer-readable medium of claim 18, wherein applyingparticle filtering to the received information from the sensor regardingthe feature vector and the retrieved feature vector particle informationassociated with two or more segments of a route of travel for thevehicle further comprises determining the distance of each particle inthe updated set of local particles to the closest segment point using aKD tree.
 20. The non-transitory, computer-readable medium of claim 19,wherein applying particle filtering to the received information from thesensor regarding the feature vector and the retrieved feature vectorparticle information associated with two or more segments of a route oftravel for the vehicle further comprises applying stochastic universalresampling to the updated set of local particles.